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Data Science: Reality Doesn't Meet Expectations

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Re: Data Science: Reality Doesn't Meet Expectations

#111

Post by downerending »

danmostudco wrote: post #43

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 math friend of mine often consulted for scientists. His least favorite were those who asked him to "make some clusters". (think k-means) "What are you looking for? What is your hypothesis?" "Just make some clusters and we'll see."

Not utterly without merit, but fairly blind fishing nonetheless.

Re: Data Science: Reality Doesn't Meet Expectations

#113

Post by mirimir »

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.

Re: Data Science: Reality Doesn't Meet Expectations

#114

Post by rixed »

resolaibohp wrote: post #42

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 ev…

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.

Re: Data Science: Reality Doesn't Meet Expectations

#115

Post by erdos4d »

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.

Re: Data Science: Reality Doesn't Meet Expectations

#116

Post by starchild_3001 »

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.

Re: Data Science: Reality Doesn't Meet Expectations

#117

Post by data4lyfe »

Barrin92 wrote: post #110

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…

There’s also the issue of data scientists just not having a seat at the table. Anyone can validate their point by using data to support their answer just like anyone can validate their opinion by doing a google search.

In my mind I see more data scientists being ignored or turned into “yes men”(https://www.interviewquery.com/blog-do-they-want-a-data-scie...)

Re: Data Science: Reality Doesn't Meet Expectations

#118

Post by kevin_thibedeau »

mirimir wrote: post #113

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…

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

Re: Data Science: Reality Doesn't Meet Expectations

#119

Post by kavalg »

danmostudco wrote: post #43

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…

As a person doing data science / ML in the last 4 years, I mostly agree with your points. Especially about the hype driven demand for DS/ML. One thing that is often neglected though is the exploration part it. There really is a lot of data out/in there that your company knows anything about, but can probably benefit from knowing. E.g. even a simple crawl of a popular jobs/ads/... site done diligently for e.g. 6 months can reveal many interesting insights about market structure and trends. Google and its mission to organize all data in the world exist for a reason. This however is in stark contrast with the approach that most executives take. Instead of managing it as a well thought strategic/long term investment, they want to time-box it, to get immediate value and to show off to senior management or customers. I've seen this tendency in both big corporations (mid-level management) and startups, which makes me think that the confounding variable is the fund/incentive management process. In both big corps and startups, there is a limited time&budget to show meaningful results and people optimize for that, which often involves taking shortcuts, neglecting strategy and outright lying. In contrast to that, I've seen projects driven by wealthy individuals, who don't look for immediate value, but are scratching an itch (e.g. curiosity). These usually fare better than the former as long as budgets don't get out of hand (to exhaust the cash cow). I would argue that these are most successful, because of better alignment of motivation (person paying the bill) and execution (person driving the process).

Re: Data Science: Reality Doesn't Meet Expectations

#120

Post by roystonvassey »

hectormalot wrote: post #5

> 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…

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.

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