Data Science: Reality Doesn't Meet Expectations

Data Science: Reality Doesn't Meet Expectations(dfrieds.com)

462 points by danielfriedman 6 years ago | 163 comments

> Moreover, you may quickly realize much of this work is repetitive and while time-consuming, is “easy”. In fact, most analyses involve a great deal of time to understand the data, clean it and organize it. You may spend a minimal amount of time doing the “fun” parts that data scientists think of: complex statistics, machine learning and experimentation with tangible results.

This. Universities and online challenges provide clean labeled data, and score on model performance. The real world will provide you... “real data” and score you (hopefully) by impact. Real data work requires much more than modeling. Understanding the data, the business and value you create are important.

As per #6, better data and model infrastructure is crucial in keeping the time spent on these activities manageable, but I do think they’re important parts of the job.

I’ve seen data science teams at other companies working for years on topics that never see production because they only saw modeling as their responsibility. Even the best data and infrastructure in the world won’t help if data scientists do not feel co-responsible for the realization of measurable value for their business.

Training integrative data professionals could be a great opportunity for bootcamps. Universities will (understandably) focus on the academically interesting topic of models, while companies will increasingly realize they need people with skills across the data value chain. I know I would be interested in such profiles. :)

kqr 6 years ago | |

I took a data visualisation class in uni that handled this really cleverly. The second assignment sounded very easy. The teacher provided links to the sources where we could find data.

Most people figured that with such a simple assignment (not significantly harder than the first one, which was also easy-ish) they could put off doing it until the last moment.

Most people failed.

This real world data needed hours upon hours of cleaning before it was in any way useable. Of course, the teacher knew this, gave bonus points to the ones who did start in time, and then extended the deadline as he had expected to from the start.

Never again will I underestimate the dirtiness of real world data. One of the best teachers I had.

starpilot 6 years ago | | |

This is universal to STEM degrees I think. In mechanical engineering classes you analyze a beam, in real life you analyze an assembly with 50 components that have undergone 100 revisions with 20 different materials and loading from 4 directions that vary with time. Oh, and you have 4 sensors to give you information to analyze critical stresses. But one of them is broken, and Bob who can fix it is on PTO until next Monday, so...

Internships are supposed to fill this gap but it'd be nice if all students could get a taste of real world systems and data. For tech, maybe if they could partner with the IT department at the school to get them exposed to real, messy data. Maybe there are some teaching datasets with over a billion rows that people could play around with.

ehnto 6 years ago | | |

Edit: the comment I replied to has since been edited to show how the teacher understood what he was doing, and how he made it teachable lesson and not a punitive one.

Not to miss the point, but I don't think "All my students failed" is the mark of a good teacher. It sounds like the teacher failed to prepare their students for the nature of the assignment. Perhaps he was surprised as they were when they all failed, as I doubt failing most of his class was his intention.

jtanderson 6 years ago | | |

I’m currently preparing a data visualization course to be taught this fall, and I would love to hear more about this! If you’d be willing to share some of those resources or the contact information for your professor, I’d really appreciate it. You can find contact info at the link in my profile :)

adenadel 6 years ago | | |

Do you still have the assignment?

proverbialbunny 6 years ago | |

>You may spend a minimal amount of time doing the “fun” parts that data scientists think of: complex statistics, machine learning and experimentation with tangible results.

I don't get why building a model people consider to be the "fun" part. That's mostly spitting data in, watching a loading screen, and then observing the output.

That's not fun, that's boring. The fun part is looking at the data and gleaming all these potential patterns from it, seeing what potential is there and what could be. Likewise, learning the business side and seeing what is possible no one has considered is great fun too.

My favorite part is feature engineering. Pre-processing and cleaning is fun too, but morphing the data into formats that extract a diamond from coal is a lot of fun, and what data science is all about. Clicking go on some ML algo is just icing on the cake, seeing it reveal bits maybe even I overlooked in the data.

If you like ML why not be an MLE? That's what MLEs do, and they're a more desirable job. DS is all about the research, discovering and learning new information, and making the impossible possible.

ims 6 years ago | | |

The standard whatever.fit(X, y) isn't very appealing but there are much more bespoke models that require creative engagement with stats/CS knowledge, e.g. Bayesian hierarchical models or deep learning models that are more complicated than what can be copy/pasted from Medium.

FridgeSeal 6 years ago | | |

MLE is a fairly new title that, as best I can tell, exists primarily in those few places that have a mature enough workflow to have people who can actually dedicate their time to the ML part and have other roles take care of the rest.

Everywhere else, there is only DS, and it involves everything.

To answer your first question though, the training and testing of these models is fun because it feels like a puzzle game: did all my understanding and preparation of the data (and the business) pay off and the model does its job as expected? Is there something I’m missing? What’s the simplest model + configuration I can use that produces acceptable results and what does that say about the problem space? Can I combine models in some way to get the results? Is nothing working because it’s an ultimately fruitless exercise and our hypothesis is wrong? Or is there something we’re missing that is in turn the reason the model is missing something? Etc etc.

Then as the output you get something that ingests some data and then makes a decision with it! That’s cool to me.

roystonvassey 6 years ago | | |

> That's not fun, that's boring. The fun part is looking at the data and gleaming all these potential patterns from it, seeing what potential is there and what could be

Exactly! This is the reason why I love my job. It gets even better when you uncover a non-intuitive insight.

itsmefaz 6 years ago | | |

Can you please elaborate on the feature engineering part a little bit?

roystonvassey 6 years ago | |

I have been in the data Analytics space for 15+ years. The one mantra I try to always focus is what’s the business impact of what our team is creating.

This is a simple yet very powerful rule that helps us quickly disband ideas that:

1. Do not have a robust testing mechanism. No model is useful unless it performs in the real world. Measuring this is a severely non-trivial problem with multiple operational considerations.

For e.g. are you able to run manage true control/test groups? How do you build a “reverse” data pipeline to verify your models? And, if you are required to update model weights constantly, where and how will you update the model parameters?

2. Conversely, some of the most impactful products I worked on were probably delivered in simple excel sheets or had just under 20 lines in my Jupyter notebook. Not every business problem is demanding a deep learning network. For e.g. we worked on a data-driven capacity forecasting exercise for a call-centre. I can tell you that the sophistication of the model was the last thing on my mind as I had to work on careful interpretation and data collection.

3. Data Science departments should sit closer to business than what appears to be the trend correctly. At least business data science teams ( Apart from technical data teams focusing on product analytics to improve performance etc ). Courses and academic programs, I think, have developed a bias towards tools and techniques without the underlying analytical interpretative techniques needed to work with data. For e.g a new data scientist in my team delivered excellent code but she couldn’t detect logical misses in the data (for e.g losing some data during processing, using columns with almost all data missing)

On the other end of this spectrum, we are in the lagging end of the hype bubble still so there are many top leaders who are expecting to plug in “data science” and realise Billions of dollars in savings, new sales etc.

semi-extrinsic 6 years ago | |

There was a remark in the old school Linear Algebra book we had in university (Edwards & Penney) that stuck with me, to the effect (probably I recall the details wrong) that one of the authors were once involved in data analysis of water samples collected from a bunch of rivers by 15 engineers, and it turned out no 6 of these engineers' measurements were internally consistent. The moral of the story was that real world data is messy, you need to learn least squares and related methods to make sense of the data.

Now with "data science" you've taken a step further, and instead of applying the math to lab reports on meticulously filled out forms, you're going to aggregate all the messy sources you can get your hands on. Of course your headaches will multiply.

avs733 6 years ago | |

>This. Universities and online challenges provide clean labeled data, and score on model performance.

First homework assignment in the stats class I teach is to clean data that the class generated with directions they all perceived as clear. It's near about the most hated assignment I have ever given. Amazing how many ways there are to encode gender of a experimental participant.

Male, M, m, male, Man, ...

plants 6 years ago | | |

gender.lower().startswith('m')... done! :)

twelfthnight 6 years ago | |

This rings true to me. I've seen a lot of models get built that are never used. Although in my experience it wasn't that data scientists didn't care about business value, it's just that data science often requires breaking down silos and asking other teams to change their behavior.

This article mentions that leadership often doesn't support data science, but I think it actually doesn't go far enough. Leadership doesn't just have to support the data scientists, it has to actually tell other teams to prioritize data science projects over what they are currently doing. Since these data science projects are riskier than standard projects, it makes sense that leadership doesn't often do this (and focusing on the standard projects could be the right call). However, it also means that it's very hard for data scientists to create business value.

throwaway713 6 years ago |

As a research-oriented data scientist at one of the larger tech companies, I can confirm that even here, a lot of people are unsure about what exactly data scientists are supposed to do. My most frequent request is "tell us why metric X dropped", to which the answer is often a subtle combination of many different factors (often random fluctuation) that doesn't lead to a pleasing actionable result in the sense of "here's why it dropped; go do this to fix it".

The really interesting research type work (Bayesian modeling, convolutional neural networks, etc.) takes a long time to implement and may produce no useful results, which is a really bad outcome at a company that measures performance in six month units of work and highly values scheduled deliverables and concrete impact. Many of the data scientists I work with tend to stick to methods that are actually quite simple (e.g., logistic regression, ARIMA) because these at least deliver something quickly, despite the fact that many of my coworkers come from research-heavy backgrounds.

In my org, there's nothing stopping anyone from pursuing advanced machine learning; for the most part we set our own agenda (in fact, determining priorities is part of the job role). And some people do in fact go after state-of-the-art ML, with some really cool results to show for it. But in terms of career progression and job safety, the risk is just way too high, at least for me personally. I save the highly mathematical stuff for a hobby.

Edit: while this may sound a bit negative, I will add that my description of data science isn't a complaint per se; I am mainly trying to inform those who are seeking a career in data science of what to expect compared to what is often promised. The work that is most valuable to a business is not exciting all of the time, but I don't think there is another job in the tech industry that I would find more enjoyable than my current one at the moment.

resolaibohp 6 years ago |

This article is pretty spot on. As someone who has worked in data science/analytics for over 6 years I have found that the field is filled with hype, managers who are not sure what data science actually is, and an absurdly wide amount of skills jobs expect you to be able to do well.

Apply for and interviewing for data science jobs is a total nightmare. You are competing against 100s or even 1000s of applicants for every job posting because someone said it was one of the sexiest careers of the 21st century. Further exacerbating this, Everyone believes that data is the new oil, and large profit multipliers are just waiting to be discovered in this virgin data that companies are sitting on. All that is missing is someone to run some neural network, or deep learning algo on it to discover the insights that nobody else can see.

The reality is that there is an army of people who know how to run these algos. MOOC's, blogs, youtube, etc have been teaching everyone how to use these python/R packages for years. The lucky few who get that coveted data science job can't wait to apply these libraries to the virgin data only to find that they have to do all kinda of data manipulating to make the algos even work, which takes days and weeks of mundane work. Finally they find out the data is so lacking that their deep learning model does very little in providing actual business value. It is overly complicated, computationally expensive, and in the back of your mind know you can get the same results using some simple logic.

Managers who don't understand data science fundamentals learn from the news and have their data scientist implement those buzz words so they can look good in front of their bosses.

I think there is a place for data scientists who understand the fundamentals of the models out there, and know when you should not use them. Data science is also increasingly a subset of software engineering and a good data science in a tech company should be able to code well. I also think that there is not some huge unmet demand for data scientists. Just a huge amount of hype and managers wanting to look good by saying they managed a data science team.

rixed 6 years ago | |

Any work is dull and depressing when done under the supervision of idiots. Some companies, although probably less than claimed, are genuinely data driven rather than HiPPO driven, though. This might be particularly important to look for theses to do interesting stuff in the fields of data science.

wenc 6 years ago |

Data science is correctly valued when you realize how relatively unimportant it is. It is a small cog in a larger machinery (or at least it ought to be).

You see, decision-making involves (1) getting data, (2) summarizing and predicting, and (3) taking action. Continuous decision-making -- the kind that leads to impact -- involves doing this repeatedly in a principled fashion, which means creating a system around the decision process.

For systems thinkers, this is analogous to a feedback control loop which includes sensor measurements + filters, controllers and actuators.

(1) involves programmers/data engineers who have to create/manage/monitor data pipelines (that often break). This the sensor + filters part, which is ~40% of the system.

(2) involves data scientists creating a model that guides the decision-making process. This is the model of the controller (not even the controller itself!), which is ~20% of the system. Having the right model is great, but as most control engineers will tell you, even having the wrong model is not as terrible as most people think because the feedback loop is self-correcting. A good-enough model is all you need.

(3) involves business/front-line people who actually implement decisions in real-life. This is where impact is delivered. ~40% of the system. This is the controller + actuator part, which makes the decisions and carries them out.

Most data scientists think their value is in creating the most accurate model possible in Jupyter. This is nice, but in real-life not really that critical because the feedback-loop inherently moderates the error when deployed in a complex, stochastic environment. The right level of optimization would be to optimize the entire decision-making control feedback loop instead of just the small part that is "data science".

p.s. data scientists who have particularly low-impact are those who focus on producing once-off reports (like consultant reports). Reports are rarely read, and often forgotten. Real impact comes from continuous decision-making and implementing actions with feedback.

Source: practicing data scientist

arborism 6 years ago | |

Had to make an account to upvote this. Absolutely dead-on. I think you can generalize this comment to almost any specialist skill. "No Silver Bullet" should be a business doctrine as well as a technical one. You need to do a lot of things well to succeed in business. Specialists just provide you a capability. You have to implement and use those capabilities as part of a larger system if you want to create a machine that generates profit.

danmostudco 6 years ago |

I stood up a data science operation at my company over the last few years, and have noticed a key difference in data-science projects that have been successful and those that have failed. It hits on a number of points brought up in the article, namely where does data science "fit" in an organization delivering software and how is the value realized by the business.

The worst cases I have seen is when executives take a problem and ask data scientists to "do some of that data science" on the problem, looking for trends, patterns, automating workflows, making recommendations, etc. This is high-level pie in the sky stuff that works well in pitch meetings and client meetings, but when it comes down to brass tacks this leaves very little vision of what is trying to be achieved and even less on a viable execution path.

More successful deployments have had a few items in common

1. A reasonably solid understanding of what the data could and couldn't do. What can we actually expect our data to achieve? What does it do well? What does it do poorly? Will we need to add other data sets? Propagate new data? How will we get or generate that data?

2. The business case or user problem was understood up front. In our most successful project, we saw users continuously miscategorized items on input and built a model to make recommendations. It greatly improved the efficacy of our ingested user data.

3. Break it into small chunks and wins. Promising a mega-model that will do all the things is never a good way to deliver aspirational data goals. Little model wins were celebrated regularly and we found homes and utility for those wins in our codebase along the way.

4. Make is accessible to other members of the company. We always ensure our models have an API that can be accessed by any other services in our ecosystem, so other feature teams can tap into data science work. There's a big difference between "I can run this model on my computer, let me output the results" and "this model can be called anywhere at any time."

While not exhaustive, a few solid fundamentals like the above I think align data science capabilities to business objectives and let the organization get "smarter" as time goes on as to what is possible and not possible.

kristjansson 6 years ago |

Teams being small, data being crummy, infra being hard, and yet expectations being high aren't so much complaints as the they are the job description.

The point of data scientists and the related roles listed in the article are not to just churn out the fun stuff, but to wade through the institutional and technical muck and mire it takes to bring the fun stuff to bear on a relevant business problem and to communicate the results in a way that people of all walks can understand.

tqi 6 years ago | |

Yeah this guy seems to think Data Science work should be like doing a problem set for CS class. Sorry that you have to deal with messy data, fragile infra, and limited resources - I know it's not "fun", but frankly that's what the money is for.

nerdponx 6 years ago | | |

That's the whole point of the article. Expectation (in this case, his own coming out of the bootcamp) vs. reality (what data science is actually like).

proverbialbunny 6 years ago | | |

The author wants to be an MLE but doesn't know it.

Eridrus 6 years ago | |

As somebody in an ML Engineering role, i.e. somebody who could be asked to either fix the logging infrastructure or build some models, I would have agreed with this.

But even in this day and age with ML being the new hotness, you will find people who are quite happy to work on infrastructure and don't have a huge amount of interest in training models themselves, and it is probably a lot easier to hire them than people who can do both, and you may get better results from actual specialists.

shoguning 6 years ago | | |

I wrestle with this too, there's a lot of context to determine what skillset is better.

I suspect, if there are lots of relatively simple ML problems, then a generalist with integration chops will be more effective in getting them out quickly and "good enough". The specialist may take too long on models that are too heavy and impractical.

If there's one big ML problem (Google search, Netflix recommender, Amazon search, etc), where 1% additional makes a difference, then yes, specialist DS/modeler is probably preferred.

Larger, older org/heavier existing infra/more specialized culture will also tilt the scale towards specialists.

Barrin92 6 years ago |

I'm generally confused by the hype around ML and 'data science'. it seems like CS has somehow regressed to the behavourism era of psychology or economics before the Lucas critique.

The problem with all this data talk isn't just about implementation or bad structure, the limitations of putting all your bets on inductive reasoning are systemic.

The insights that economists had in the 70s and 80s was that reasoning from aggregated quantities is extremely limited. Without understanding at a structural level the generators of your data, trying to create policy based on outputs is like trying to reason about inhabitants of a city by looking at light pollution from the sky.

My guess why data science so rarely delivers what it promises is because you can't get any value from historical data if your circumstances change to the point where past data is irrelevant. Which in the world of business happens pretty quickly. To have a competitive advantage, one needs to figure out what has not been seen yet.

And trying to exploit signals suffers from the issue laid out above. There was a funny case of an AI hiring startup trying to predict good applicants, and the result was people putting "Oxford" in their application in a font matching the background color

analog31 6 years ago |

As a scientist, I've worked with data for decades. There's always been a prevailing belief that scientists and engineers with specialized domain knowledge are mostly fumbling in the dark and can be replaced with a general purpose technique.

This was certainly the vibe that I got from "design of experiments" when it was the statistical method du jour. Then from "Bayesian everything" and now "data science." I remember "design of experiments" studies being conducted with great fanfare and success theater, while producing zero results.

The long term theme is that science is hard for reasons that managers don't understand, can't manage, and are reluctant to reward.

rafiki6 6 years ago |

I've seen a few similar articles now. Does this represent the general view of folks working in data science? "Data Science" is such as meaningless catch all term. The reality is in many organizations it's simply advanced business intelligence or advanced business analytics. There are some industries that lend themselves well to this whole practice, and they tend to be industries that have been borne out of the internet age (e.g. social media, internet advertising, etc.)

Some other industries have been doing "data science" for ages. Credit Risk Modelling, insurance and so on.

Every time I read one of these articles, I feel it's just an individual who entered a kind of crummy situation and they're learning what it means to work in a corporate environment. Some are better than others. Some are more motivated than others. Some have better cultures than others. Some are more willing to make technology a key part of their business strategy. Some are more data driven than others.

My recommendation is to always ask the fundamental question before joining: what are you trying to achieve with data science, and is it actually achievable?

smeeth 6 years ago | |

I always thought the non-specificity of the term Data Science was a strange criticism for those in the tech industry to make. How many types of SWE are there? Front-end, back-end, full-stack, devops, security, QA...

I agree wholeheartedly with your recommendation. Like any other job, each company has different needs and expectations and if you want something else out of the role you'd best avoid that company.

rafiki6 6 years ago | | |

Frankly I have the same criticism of those who use the term software engineer. Engineering is a pretty established profession with a set of standards, ethics and practices. Most of us who work in software are not engineers. We are developers. Similarly, a scientist is one who follows the scientific method to do research. So by that logic a data scientist should be a person who uses the scientific method to do research on data. Does that make any sense? And let's be serious, is that what most data scientists are being hired to do?

antipaul 6 years ago | |

100% agree with the article. The top misconceptions are spot on. I’m at a big place where data science hype among leadership couldn’t be bigger.

rafiki6 6 years ago | | |

If I may ask, what were you told when you interviewed that convinced you to join?

Optimal_Persona 6 years ago |

As a data dude in public/nonprofit healthcare-landia I agree with all this, plus:

- It's essential to have/develop domain expertise in your industry.

- Beware plausible, but incorrect (or poorly interpreted) data that supports yours (or others') assumptions/biases.

- Add on to #4 - at least as bad as this is having well-intentioned people on your team who "know enough (a bit of SQL or low/no-code data tool") to be dangerous. Um, why are you joining unnecessary tables, or using a different alias for the same columns/tables in different queries, with no comments or standard formatting?

- Hold your nose, but anything you do in SQL/R/Python/even fancier programming tool/language is going to pass through MS Excel at least once sooner or later which can irreversibly bastardize CSVs (even just opening without saving!), truncate precision to 15 digits, change data types, etc.

- So glad for the callout in #7 - there are clearly devs/data folks out there who are happy to take on an "interesting programming project at a great paying job" - that isn't serving the best interests of humanity.

Icathian 6 years ago |

This rings very true to me. I'm working on moving over to an SWE role in the next few years for many of these reasons.

I'll just add one: the business absolutely doesn't care how you get your answer, only if they're reliable enough (hand grenade close is better than most companies have today).

While this seems obvious enough to anyone with a few years under their belt, to the new DS grad who has their time series analysis canned in favor of slapping a simple moving average in place and shipping it can be rather disillusioning.

Vaslo 6 years ago |

I’ve been doing an MS in Data Science very slowly due to work and 2 new kids. Finishing the degree this year in year 4. I was very excited about the prospect of doing something different. A few things have changed for me.

1). I am hearing about Data Science Teams being furloughed during these times. That isn’t happening in my function (Corporate Finance). I am glad to be secure even though I enjoy much of the data sci work.

2) I’m able to apply Data Science concepts in my current role, and it’s adding a lot of job security and providing me with exposure. I am much less interested now in moving to straight Data Science and instead am applying my learnings in my current role as a sort of in-house Data Science guy. But I have a lot to learn to be honest.

3). There seem to be a lot of “thought leaders” acting like they are big experts in the area and really don’t know anything many of us amateur scientists don’t know. They pull perfect clean datasets and show these magic transformations they just copy from others to get YouTube hits or Twitter followers. That just never happens in real life, and many leaders are seeing this and losing interest in this function in the returns they are getting from sole data science folks.

RayVR 6 years ago | |

This isn’t unique to data science. I personally know people in finance that are poor coders and even worse quants, yet they go around lecturing at universities.

s1t5 6 years ago |

I work as a data scientist. Some of the author's points are workplace-specific: lack of leadership, being the only data person, ethical concerns. The others are just aspects of the job - communicating about your job and impact, dealing with vague specs or managing low-quality datasets.

Neither of those quite match the articles title, perhaps it just refers to the author's personal expectations. Neither of them seem that specific to data science, or without parallels in other software jobs. And neither of the points read like a slight towards data science to me, like some of the other commenters here suggest.

UweSchmidt 6 years ago |

One issue might be that organizations subconsciously resist the data scientist, or more generally, the nerd in his/her attempt to take over decisions. If these decisions are invariably tied to the goals and careers of managers, how can the data scientist have a "seat at the table" in all but the most enlightened and technical companies? The disorganized state of data and infrastructure suits the ambitious manager well, who can just put in enough effort to find data to have their project greenlightened or to answer one specific question.

Progress may only come slowly, ideally through products bought from 3rd parties whose results are understood and controlled by management.

mirimir 6 years ago |

I did "data science" for about a decade, consulting with plaintiffs firms and state AGs on antitrust and fraud cases. For each case, the work flow was roughly this:

-- write discovery requests

-- review production, and check out data and documentation

-- write supplementary discovery requests

-- review production, and check out data and documentation

[repeat as needed]

-- analyze data, and write deposition questions

-- help attorneys wring answers from deponents

[repeat as needed]

-- analyze data, and produce required output

-- write parts of briefs and expert reports

I generally did that in consultation with testimonial experts and their data analysts. Sometimes that didn't happen until we'd documented the case enough to know that it was worth it. And occasionally small cases settled with just me as the "expert".

It's a small industry, and not easy to get into, unless you know key players at key firms. But the money's pretty good, and the work can be exciting. I loved being that guy in depositions whispering questions to the attorneys :)

This all involved pretty simple calculation of damages, through comparing what actually happened vs what would have happened but for the illegal behavior. But-for models were typically based on benchmarks.

After data cleanup in UltraEdit, I did most of the analysis in SQL Server. I used Excel for charting and final calculations.

kevin_thibedeau 6 years ago | |

I would expect "data science" is doing some form of numerical analysis. Otherwise it's just record keeping... with computers.

mirimir 6 years ago | | |

The hardest part of what I did was getting enough documentation to understand the data. Sometimes we got fixed width text files, with no in formation about column definitions. Or column names. Or what values in descriptive columns meant. Stuff like "class of trade".

But generally you're right. It was just simple calculations using sales records. But lots of records, at least several gigabytes, and sometimes several hundred gigabytes.

beckingz 6 years ago | | |

Record keeping is 90% of data projects.

The second 90% is basic math at high speeds.

avip 6 years ago |

This reads like Indiana Jones teaching Archeology. Yes, as a data-scientist you actually have to work, most of the work is digging in dirt, and mostly you won't find anything of interest.

op03 6 years ago |

It works well when subject matter experts exist in the org and collaborate/supervise/drive data folk, to solve some issue the sme's have spent enough of their own time thinking about.

If its just data folk by themselves getting dumped with org data and told to find pirate gold...then its a crap shoot.

agentofoblivion 6 years ago |

This reads as a series of bad job experiences and I think is explained by a wide variety of job functions that all can have "Data Scientist" as a title. Someone else's experience could be totally different. You have to know what to look for and what to avoid. If you're trying to find a DS job, one of your top priorities is finding out what the actual job consists of. For instance, a Data Scientist at Facebook might be called a Data Analyst at many other places--no modeling required.

I know this because I've been on that journey. But there's no reason to expect some department head that's never been exposed to DS to know this. They just copy/paste some other company's job req. If you're more junior, here are my tips:

- If it's a "new DS team" that supports a variety of teams: beware. Bolt-on DS doesn't work well, as it's really hard to build a meaningful solution that's not deeply integrated.

- If it's an old company or in a conservative industry: beware. There are likely to be data silos and difficult ownership models that make it nearly impossible to get and join the data you need.

- If it's a small company: beware. You're likely going to need a broad set of knowledge that's won with several years of experience to be able to build end-to-end solutions that are integrated into the rest of the tech stack.

- If it's not an engineering-driven culture: beware. DS will often be used to provide evidence to someone else whose already made up their mind and pretend they're being data-driven, and you'll be the disrespected nerd that's expected to do what it takes to deliver the answer they want. Most companies claim to be "data-driven", few are, and even fewer understand data-driven isn't always possible or desirable.

Industry is still trying to figure out how to use ML and are still learning that it's not as easy as hiring someone that knows about all the algorithms, but rather it takes deep technological changes to data infrastructure to enable the datasets that can then be used by the ML experts. But you don't have to be the person that helps them figure this out the hard way (i.e. by being paid to not accomplish much due to problems outside of your control). Better to find a place with a healthy data science team that can help you learn and contribute. They exist.

deppp 6 years ago |

Great read. A lot of those problems are real, and some of those I’ve experienced myself. But I think at least some of them are related to the immaturity of the field. We’re only at the beginning of creating the tools and platforms to facilitate DS, making it more reproducible and easier to measure.

For example, I’m working on the tool to make data management easier and convert datasets into a structured representation. If you have experienced that you spend a lot of time on preparing and analyzing data, and it is tedious, please reach out to me michael at heartex.net, would love to get your feedback on the product we have built so far.

leto_ii 6 years ago | |

> But I think at least some of them are related to the immaturity of the field.

I agree. More so, I sometimes feel that in the end the field will break up once things start settling down. Some roles will migrate more towards engineering, some will go back towards data analysis.

The expectation that a data scientist is a funnel that can turn anything into magical insights and tools can't last forever.

AndrewKemendo 6 years ago |

A really easy way that I try to explain things to people is like this:

You can't compress information until you have it in a format that is appropriate for compression.

That is:

You can't compress (apply/create algorithms) information (data) until you have it (instrumented data collection) in a format (schema) that is appropriate for efficient compression (structured logging/cleaning).

99% of that is Data Engineering and building good engineering practices which have good data practices as a priority.

For any organization that has more than a handful of employees and more than one product, that is a non trivial task and gets more difficult the larger the organization gets.

antipaul 6 years ago | |

Totally agree. Non-tech companies that think they need "data science" should instead put same effort into (data) engineering.

It's not quite 99% of the effort but close enough ;)

Search "data science hierarchy of needs"

minimaxir 6 years ago |

I wrote a blog post along similar lines in 2018 (https://minimaxir.com/2018/10/data-science-protips/ ); unfortunately, the industry hasn't changed much since then.

As noted in the submission, there's a lot of flexibility in what a "data scientist" is. Normally that's good and healthy for the industry. However, it contradicts a lot of optimistic bootcamps/Medium/YouTube videos, and many won't be prepared for the difference.

freehunter 6 years ago | |

My industry (information security) is the same way. Far too broad of a category, leading a lot of people to get confused and frustrated. I see a lot of analysts (defensive security) who thought they were going to be pen testers (offensive security) and didn't realize those jobs were in two completely separate career paths.

stuxnet79 6 years ago |

I've been a data person for the past year and a half and I'm very disappointed with the bewildering array of titles out there and the rather vague meanings behind them (Data Analyst, Data Scientist, Data Engineer, ML Engineer).

It's overall hurting my ability to build my personal brand and seek roles that are a fit for my existing skillset and aspirations.

What exactly does 'ML Engineer' communicate to employers in terms of baseline skills? Is the role closer to that of a data engineer or an analyst?

dtjones 6 years ago | |

I've been working in data roles for 10 years and hold a masters in ML. I've hired and managed each of the roles you mentioned. I think of the responsibilities of each of those roles as:

-ML Engineers as building software infrastructure to scale machine learning inference and training.

-Data engineers focusing on data infrastructure and pipelining into either model inference, training, or other business intelligence platforms

-Analysts consume the product of the data engineer in the BI platform or excel, where the results would be consumed as a report in some form.

-And ML Researchers would be those inventing novel machine learning algorithms to deploy in the ML Infrastructure managed by the ML Engineers

-And data scientists to deploy well-known ML algorithms or statistical inference on varying datasets on the ML Infrascturue or as a slide deck.

beckingz 6 years ago | | |

How hard could it be to find one person who can do all that?

stuxnet79 6 years ago | | |

Thanks for the breakdown!

starpilot 6 years ago | |

This reminds of the latter days of the LAMP stack. A "web developer" might do front/backend and sysadmin work. I think some people see "data scientist" similarly, wearing many (all) hats, which can work for some environments, but not most corporate ones.

arafa 6 years ago |

From my perspective as a data person, everything on this list is true. I would add on to #4 to say "You're likely the only data person" to say "You're likely the only data person and expected to do everything you need to do your job yourself" (from sourcing the data to deploying your model).

High Data Scientist salaries and expectations combined with a shortage of qualified people often mean you're expected to be a one-person band, which I find to be miserable.

ajeet_dhaliwal 6 years ago |

Point 5, “ Your impact is tough to measure” is also shared by Quality Engineering and SRE, and not unique to Data Science. The point about being a support role holds true for them and it is thoroughly frustrating when a front-end dev makes a small change to a visual element is praised to the roof while complex automation projects by the quality team, ingenious recovery and reliability projects by SRE, and massive and fascinating inferences by data science are undervalued by leadership. The truth is most leaders just can’t connect the dots. I’ve worked as a full stack engineer btw do not taking a dig at front-end work, but it’s clearly easier to measure impact. I’ve worked in quality too and when you’re only called in to ask why one bug got out and never asked about the thousands you’ve stopped it’s demoralizing. It’s part of the reason I started Tesults (https://www.tesults.com), if you’re in one of these support roles, measure, measure and measure and throw those reports into the faces of leadership. It shouldn’t have to be done but without it, the point the author is making here will take place.

FridgeSeal 6 years ago | |

> and it is thoroughly frustrating when a front-end dev makes a small change to a visual element is praised to the roof while complex automation projects by the quality team, ingenious recovery and reliability projects by SRE, and massive and fascinating inferences by data science are undervalued by leadership.

I feel this in my bones lol.

The frustration when the results of weeks/months of hard work are glossed over with a “oh that’s nice” in favour of endless praise for the front end team putting a picture backdrop on the search page or something.

Didn’t matter how many times we sold them on the benefits, or explained the work that went into it (at both executive summary level and detail) or did all those things you’re supposed to do, if it was more than one step away from directly causing it, or slightly more abstract than “we moved the button” it was wasted on leadership/management.

Spent a couple of weeks fixing data pipelines and ETL/database infrastructure and processes and now everything runs faster, and runs on a smaller and cheaper cluster and as a result managed to put together some analysis and modelling on customer behaviour that shows if you do xyz you’d expect to see uptick in this thing. Doesn’t matter, Bob changed where the button sits and we saw 20% more sales, good job Bob, everyone: be more like Bob.

smitty1e 6 years ago |

Coase: "If you torture the data long enough, it will confess to anything."

via https://www.reddit.com/r/QuotesPorn/comments/b76ujr/if_you_t...

FridgeSeal 6 years ago |

> You’re likely the only “data person... Because people don’t know what data science does, you may have to support yourself with work in devops, software engineering, data engineering, etc.

Nothing has summed up my entire working experience more than this, it’s almost painfully accurate.

On one hand it’s an exciting challenge, you learn a lot and you get good at adapting to these situations.

On the downside I have practically no senior data science people to turn to for help when I do need it, which is frustrating.

mikorym 6 years ago |

I am sorry to sound like I am being obstinate, but my opinion about this is that as a society, since the early 90s, we have put way to much focus on "tech" than we have put on plain old mathematics or foundational science.

I don't mean manufacturing (which is doing really well), but companies like Microsoft, Google, Facebook (and even Apple) and others do encourage you to try to compete against their founders (or maybe society does that) rather than focusing on being solid mathematically. Yes, Google pays people well with those skills, but movies portray mostly their founders, emphasising how rich they are, while mathematicians are generally portrayed as weird. Society as a whole puts more emphasis on Bill Gates than on fundamental researchers.

In fact, if you really want to have a rich representative, you can pick the Simons guy. (See, I don't even know his name.) His Medallion hedge fund was built on mathematics. Ironically, Bill Gates is these days one of the biggest financial supporters of people with science skills that he doesn't have.

It is a fad to be a techie. Mathematics is not a fad, although it does have internal fads.

worik 6 years ago |

I do not understand. Have never understood. "Data Science" is, surly, newspeak. The appropriate term, surly, is "statistics".

antipaul 6 years ago | |

There are some differences between stats and data science.

At least initially, DS was a lot about machine learning. While those methods may be statistical, it was the computer science field that drove and embraced the ML revolution. Currently, it’s mostly ML engineers who make the impact (deploy) ML and these are mostly CS folks. Statisticians still can’t code themselves out of a box (2013 MS Statistics here from top school)

Also, there has been a lot of innovation in managing data at scale (tools, infra , etc) This, again, has been done by engineers not statisticians. But this is still related to the science of data.

So the difference between the new (data science) and the old (stats) is about culture and about some of the methods for dealing with data at “scale”.

In other words, statistics is just a part of data science, but not the whole.

wenc 6 years ago | |

Data science is an overloaded term, but even so there are some salient differences between it and statistics.

Data science more closely related to "statistical learning" and the knowledge required overlaps with but looks quite different with that of conventional statistics.

An easy way to get a sense of the difference is to compare the table of contents of a book like ISL (PDF free) [1] to the undergraduate curriculum of a statistics program. You'll find that that the focus and indeed culture of data science is really quite different from that of statistics.

Leo Breiman wrote about this in his paper "Statistical Modeling: the Two Cultures" [2]. Conventional statistics belongs to one culture, and statistical learning/data science sort of veers toward to the other (though not completely).

Much has been made about how "data science" is just statistics dressed up to look new, but I'm not convinced this is true. I'm also not convinced that pure statisticians have the right training to be data scientists -- additional training and mindset changes are needed. The reverse is also true: most data scientists lack the rigor and epistemological training to be statisticians.

[1] http://faculty.marshall.usc.edu/gareth-james/ISL/

[2] https://projecteuclid.org/euclid.ss/1009213726

_jal 6 years ago | |

A new title means a new opportunity to ask for more money and influence. See also, "devops".

beckingz 6 years ago | | |

Going from IT to devops is a great way to double your salary.

diehunde 6 years ago | | |

Or microservices developer/architect

leto_ii 6 years ago | |

Indeed, perhaps applied statistics or even data analysis. It has always felt stupid calling myself a data scientist, but the term statistician has certain connotations that are not always relevant for the corporate context.

blawson 6 years ago | |

https://twitter.com/cdixon/status/428914681911070720?lang=en

erdos4d 6 years ago |

I worked as a data scientist for 4 months at a VC firm. I have a PhD and thought the work might be legit when I was hired. After the 4 months I quit when it became apparent that my credentials were being used for managerial intrigue and the work was essentially a joke, with no rigor at all. This article hits the nail on the head, unfortunately, these positions are not often real jobs.

zeveb 6 years ago |

> internally, you can make inroads supporting stakeholders with evidence for their decisions!

The problem is that this can all too easily become motivated reasoning: one provides a stakeholder with support for the decision he already made. From his point of view, this is a valuable service, but it does the organisation a disservice: decisions should be made after considering the data, rather than consider only those data which support a decision.

Also, while ethical issues certainly arise, I think that Greyball is not a good example. Uber evading police enforcing the taxi monopoly is no more unethical than the Underground Railroad evading fugitive-slave agents. The taxi monopoly is itself unethical, and evading it increases the common good.

sgt101 6 years ago |

Disclaimer: I use the term Data Scientist throughout this post; however, popular titles such as Data Analyst, Data Engineers and BI analyst are randomly applied by people who know nothing, and these people share none of the responsibilities of a Data Scientist.

I have never had hopes about the potential impact of being a Data Scientist. I felt every company should be a “data company”, but everything I knew told me that companies are political institutions bounded by the pressures of late stage capitalism. Anyone who things different is dim, anyone who blogs about it is a moron.

My expectations did meet reality.

Where did my expectations come from?

I attended a four year Computer Science degree, followed by four and a half years of earning a Ph.D. I then spent 20 years in industry. 19 of the 20 weeks’ focus were not on machine learning (ML) and artificial intelligence (AI).

I figured I’d spend most of my time buried in code and data, I was right, I had to find shit buried in it, and dig it out with my teeth. Executives hated me because I was a threat, but they needed me so I continued to get paid. I continue to be able to create insight and predictions that almost no one else can, and until this stops I will get a 200k a year salary, benefits and a Tesla.

All of this happened, I can't be bothered to waste my time commenting on this moronic blog post.

vasili111 6 years ago | |

How important is Ph.D for data science?

sgt101 6 years ago | | |

I think it's quite important - or an equivalent.

From about 2012 to 2018 I went round a lot of universities, conferences and companies doing presentations and I used to often ask the audience for a definition of data science (in the hope of getting a good one). The best one I heard came at the University of Bath where someone (I know who, but he didn't say it to back it with his reputation so it's not fair to name him - it wasn't me though) said "Just drop the data, it's science".

I totally think that - Data Science is about doing Science with found and evolving data sources, we aren't often able to construct our experiments from scratch, but we often get to augment them, but we always start from the data we are given - which is why it's a sub-field.

In any case - the Ph.D's I have employed have almost all known how to do Science, and it has really helped. Some people without a Ph.D. learn to do it. Experimental Ph.D's are best.

Maths and theoretical Physics Ph.D's are generally not able to do this!

Ididntdothis 6 years ago |

Work reality rarely meets expectations. I am sure a lot of “UX” people also got a little disappointed that reality is much more mundane than the fancy title suggests. Or the people whose last algorithm work was the interview.

DrNuke 6 years ago |

It really depends on the niche or the industry considered, though: I can happily say that I can do materials informatics from my basement at home now and much faster and better than as a cog in any lab anywhere in the world. Same for a great number of STEM applications, if you ask or follow high-level practitioners through conferences, journals and social media. The elephant in the room is the Intellectual Property generated through STEM applied data science, which is hot and even dangerous as you can see from superstars like OpenAI, DeepMind or politically-motivated aggregations.

kovac 6 years ago |

The most common complaint I've heard from the data science team is that there isn't enough data to work with.

I'm not fully convinced that data science with ML and more modern techniques are applicable across domains out of the box. I think there is value to be added if data scientists can specialise in domains.

If we take humans as an analogy, even with the kind of general intelligence we have, we need domain expertise to be able to have advanced intuitions and make predictions about the future. I believe this is true for data science as well.

starchild_3001 6 years ago |

I was a data scientist for one year. Experienced many of the adverse situations explained in the article, plus I thought it isn't for me. I joined my next job as a software engineer (after an extensive interview prep). Couldn't be happier. Still doing plenty of data science. But my product is actually a product, not the analysis (as is often the case with DS). I feel "more central" to the project, to the company. I'm still building ML models, features etc for a living.

StonyRhetoric 6 years ago |

As the lead data scientist at a small-ish fintech, I can confirm many of the frustrations and disappointments in the OP. But my trajectory was slightly different - from being the only "data science guy" in 2016, to now leading an autonomous team of four, with quarterly meetings with the CEO, and monthly meetings with our tech leadership. I decide tech stack, workflow, and hiring. Execs decide priorities. Sure, some of it was dumb luck, some of it was actually having a CEO that cares about data strategy, but I like to think at least some of it was me.

So here's what I think I did right:

1. Provide indisputable, obvious business value every month. You should consider yourself an in-house consultant to whichever cost center your salary is drawn from. If you're product development, prove value to them. If you're operations, or sales, or marketing, prove value to them. After about two months, you should be able to justify your existence in two sentences. Just remember, most of your company probably thinks of you as a optional add-on.

Your first few projects should attack high-impact pain points with the simplest solutions possible. My first projects were basically ETL into some basic regression into a dashboard. No machine learning required. But it was better then what they had (which was often nothing), and it was STABLE and RELIABLE. And that leads to the next point...

2. Build trust. With my dead-simple models, nothing ever blew up, there were no nonsensical answers, and there wasn't much brittleness when new categorical features or more cardinality was added. It mostly just worked. And that built my reputation for me. They didn't have to understand what was going on in the model, but they knew, from experience, that they could trust the result. Once I had the credibility, I could start building more complex, more elaborate models, and asked them to trust those as well. If they don't trust your models, then no business value has been created, and your job is worthless.

3. Recognize that data science is being done everywhere in the organization, and respect it. Every department has someone who has built a monster spreadsheet that contains more embedded domain knowledge then you could hope to learn in a month. As data scientists, we like to think that we're helping the organization by building critical metrics to improve performance. But here's the catch. If the metric was truly critical, someone has built it already. It might be ad-hoc, use poor-methodology, and be somewhat wrong, but it works and is good enough. You have to find that person, learn from them, and improve on it.

4. Be as self-contained as possible. Ideally, your critical path should not depend on other teams doing things for you (except for IT setting up data access). You should be able to do it all. From front-end dashboards, to ETL, to DevOps. Remember, you're an in-house consultancy. You should be able to take problems and just handle them, rather then be a perpetual bother and distraction to other teams.

There's more, but if you do these four things, I think you can build the reputation in your company for creating useful, accurate data tools that help other people do their jobs better. After that's achieved, people will breaking down your door to get your help. That's where my team is now - we've got a backlog for at least 18 months, with our work priorities often being set directly by the CEO.

simonkafan 6 years ago |

My feeling is that a lot of companies think: "We need a data scientist because all the big players also have one!"

In fact, they actually don't need a data scientist. At best they need someone who cleans data, creates pie charts or even worse, they relabel the database admin job as "Data scientist".

Rainymood 6 years ago |

Can definitely relate to this. Work for big consulting firm (F500) as a data scientist, end up in this weird software engineer/ml engineer hybrid role.

I personally love it but am doing more pure software engineering now as the infrastructure is not there and I need to build it myself.

alixedi 6 years ago |

To point #5 in the article, in my experience, ascending order of potential to generate value for business:

An astonishingly large fraction of Data Science output goes to die in pretty presentations.

From what's left, a large fraction ends up in Spreadsheets.

A disappointingly small fraction ends up in live services.

new_learner 6 years ago |

Completely agree. I made a post couple of weeks ago trying to find some solutions for this: https://news.ycombinator.com/item?id=22673236

dzonga 6 years ago |

it's a problem with tech in general. some things come over-hyped. and in the process people forget what's the actual problem to be solved because they fell in love with tools | tech. maybe the solution could easily be done in excel but then that's not sexy. I personally prefer to handle most parts in Python because of automation. writing functions in python is easier than writing functions in SQL or Excel(macros)

arrozconcosas 6 years ago |

Maybe I have a narrow set of experience, but in my mind a “data engineer” is not a substitute for a “data scientist”.

graycat 6 years ago |

The situation sounds similar to ones years ago for statistics, operations research, optimization, and management science.

I view all of such work as applied math.

My experience is that applied math, from the fields I mentioned and some more recent ones, and more, with emphasis on the more, can be valuable and result in attention, usage, and maybe money.

I've had such good results and have seen more by others.

Some examples:

(1) Airline fleet scheduling and crew scheduling long were important, taken seriously, pursued heavily, with results visible and wanted all the way up to the C-suite.

(2) Similarly for optimization for operating oil refineries: So, here is the inventory of the crude oil inputs and the prices of the possible outputs. Now what outputs to make? The first cut, decades ago, was linear programming, and IBM sold some big blue boxes for that. More recently the work has been nonlinear programming.

(3) The rumors are, and I believe some of them, that linear programming is just accepted, used everyday, in mixing animal feed.

No surprise and common enough, IMHO what really talks is money. If can save significant bucks and clearly demonstrate that, then can be taken seriously.

But from 50,000 feet up, tough to get rich saving money for others. If they have a $100 million project and you save them $10 million, then maybe you will get a raise.

What's better, quite generally in US careers, is to start, own, and run a successful business. If that business is to supply the results of some applied math, and the results pass the KFC test, "finger lick'n good", then charge what the work is worth.

Maybe now Internet ad targeting is an example.

I'm doing a startup, a Web site. The crucial enabling core of what I'm doing has some advanced pure math and some applied math I derived. Users won't be aware of anything mathematical. But if users really like the site, then it will be mostly because of the math. So, it's some math -- not really statistics, operations research, optimization, machine learning, artificial intelligence, or management science -- it's just some math. The research libraries have rows and rows of racks of math; I'm using some of it and have derived some more.

Generally I found that the best customer for math is US national security, especially near DC. E.g., now some people are building models to predict the growth of COVID-19. Likely the core of that work is continuous time, discrete state space Markov processes, maybe subordinated to Poisson processes. Okay: One of the military projects I did was to evaluate the survivability of the US SSBN (ballistic missile firing submarines) under a special scenario of global nuclear war limited to sea -- a continuous time, discrete state space Markov process subordinated to a Poisson process. Another project was to measure the power spectra of ocean waves and, then, generate sample paths with that power spectrum -- for some submarines. There was some more applied math in nonlinear game theory of nuclear war.

Here's some applied math, curiously also related to the COVID-19 pandemic: Predict revenue for FedEx. So, for time t, let y(t) be the revenue per day at time t. Let b be the total market. Assume growth via virality, i.e., word of mouth advertising from current customers communicating with remaining target customers. So, ..., get the simple first order differential equation, for some k,

y'(t) = k y(t) (b - y(t))

where the solution is the logistic curve which can also be applied to make predictions for epidemics. This little puppy pleased the FedEx BoD and saved the company. Now, what was that, data science, AI, ML, OR, MS, optimization? Nope -- just some applied math.

I have high hopes for the importance, relevance, power, fortunes from applied math, but can't pick good applications like apples from a three.

scottlocklin 6 years ago |

> I attended a 12-week data science bootcamp in mid-2016. ...

Yeah, well there's your problem, my dude. I've been doing what might be described as "data science" since I quit physics in 2004. Aka before the term existed. It's a great area to work in for intelligent people who want to use their brains to impact the real world; vastly better than what people get paid to do in physics. If customers don't know what the tools can do, it's because you as the data scientist have failed to explain it to the customer. If your work product isn't in front of the decision makers, you've also failed: they can tell the bottom line impact and will reward you accordingly. Sometimes there is no data in their data; they should know that up front.

As for whining about poor data quality: n00b. What do you think they're paying you for? Nobody gives a shit what people do in Kaggle competitions.

chosenbreed37 6 years ago | |

I don't think the op would care much for your delivery but you make some great points.

> If your work product isn't in front of the decision makers, you've also failed: they can tell the bottom line impact and will reward you accordingly.

This one in particular stood out. There is an aspect of salesmanship (or navigating corporate hierarchies) to the role. Things will not be obvious to the decision makers. Perhaps the data scientist has to take some responsibility in bringing their work to the fore.

codingslave 6 years ago |

The real issue with data science, from the perspective of ML pipelines/using ml in products, is most people are straight up not smart enough for it. The second the problem falls outside the bounds of a commonly used model, 90% of data scientists are ill equipped to come up with a profitable solution. So they stumble around in the dark, producing nothing of real value. People underestimate the degree to which extreme mathematical maturity and skill can bend the results of commonly used ml models.

eanzenberg 6 years ago |

I guess I'm in the minority in these threads..? I've been doing machine learning / model-building / pushing models to prod and maintaining for about 6 years now. It's still 50/50 understanding the data and building/tweaking/training/testing models. But it sounds like most people with this title are analysts? At least that's what posts and threads lead me to believe. I've also met a lot of people with titles like "ML Engineer" or "Data Scientist" who don't do machine learning. They are analysts, engineers, or maintaining data pipelines.

proverbialbunny 6 years ago | |

Pushing models to prod is often MLE work depending on the organization, though MLE is often slang like how dev is slang. MLE an be a job title just as Developer can be a job title, but more common than not the job title is Machine Learning Software Engineer, or just Software Engineer for short.

I suspect a lot of people want the sexy Data Science job title, which is why there has been such a push for it, and why most new "data scientists" take the title but do Data Engineer / Infrastructure Software Engineer work or MLE work instead.

I think MLE is more sexy in a lot of ways, and it often pays better than DS work, so it's odd that many haven't flocked to that job title, but maybe the whole software engineer part turns people off for some sort of reason.

Me, I'm more a classic data scientist / research engineer, which involves a lot of digging through data and research and generalized learning, then presenting my findings. I'm not using any ML on the job right now, but often I have in the past. It's just a tool, not an end.

johndoe42377 6 years ago |

There is a philosophical principle which says that any model superimposed on reality could be seen as reality itself, while it is merely a superimposed interpretation, in principle.

Korzybski formulated these principles, among other things.

Most of data science models are as wrong as astrology and numerology. They have no connection to reality, or rather inadequate.

This principle explains abysmal failures of all Model-based "sciences", stating from financial markets and up to virus spreading models.

Simulations of non-discrete, non-fully-observable (AI terminology) system has exactly the same relationships with underlying reality as a Disney cartoon to a real world.

This is why expectations will never be meet, except for natural (non-inaginary) pattern recognition.

A drop of proper philosophy worth years of virtue signalling.