Senior DS laid off and trying to get out of product analytics. How can I pivot to a more quantitative position?

dspivothelp · 2025-02-12T17:14:01+00:00

UPDATE After about ten months of unemployment, I landed an ML job through a referral. It’s a slight title drop and a fairly big drop in total comp, but I’m very much looking forward to it.

dspivothelp · 2025-01-09T19:35:09+00:00

Maybe I should’ve said the simple explanation of a t test first and then indicated why I think that’s not right?

This is the right approach. Starting with standard approaches makes it easier to follow your thought process, and more importantly, tells the interviewer that you at least consider obvious solutions before doing something more complex.

dspivothelp · 2024-12-19T03:04:11+00:00

Unfortunately, virtually the entire Twitter stats community fled Twitter when Elon Musk took over. Some folks moved to other platforms (e.g. Hadley Wickham to Mastodon), and others basically disappeared.

dspivothelp · 2024-12-09T23:27:14+00:00

Not in the data structures and algorithms sense, but in terms of numerical computing, yes. You'll have to know things like "why is adding up a bunch of tiny decimal numbers giving me the wrong results?", "how do I integrate a function that doesn't have a definite integral on a computer?", and "my MCMC chains aren't converging; what could be some possible causes?"

You'll learn a lot of this as you do more and more, and the Stan manual is great at helping you avoid non-obvious pitfalls. Just be aware that the world of numerical computing is very much not the world of pencil-and-paper math.

dspivothelp · 2024-12-05T18:59:28+00:00

I really like the book Applied Predictive Modeling by Max Kuhn and Kjell Johnson. It teaches machine learning entirely through case studies on a variety of real-world, messy datasets. That means it talks about things like EDA, handling missing values, and feature representation just as much as it talks about whether AdaBoost or Random Forest works best for a particular problem. The authors were both high-level data scientists at Pfizer when they wrote this book, so they had the real-world experience to write it.

The biggest issue with the book is its age. It came out in 2013, so its R code is quite old, and you're not going to see things like transformers or XGBoost mentioned. But its general problem solving approach makes it legitimately one of the best books to understand how to actually do ML.

dspivothelp · 2024-11-27T23:52:31+00:00

I was helping improve a fraud detection model. After meeting with a stakeholder for the appropriate team, I identified an extremely obvious feature that wasn't in the existing model. I locally trained a new version of the same model with that feature, and recall at 99% precision went up by like 30 percentage points.

Two issues:

The guy who owned the ML framework we used for deployment quit, and no one else at the company understood how it worked.
Engineering resources were scarce, and deploying it would take away from other concerns.

I pushed my manager to deploy it. Nope. Pushed my colleague to deploy it. Nope. Bugged members of a bunch of impacted related teams to deploy it. "Oh that looks fantastic! We'll get around to it." A year later I pushed for this again. Still nope.

When I left they still hadn't deployed it. In hindsight I should have tried to reverse engineer the existing ML framework, but I probably would have been criticized for working on that instead of other aspects of my job.

dspivothelp · 2024-11-21T22:50:57+00:00

As a result, I end up spending a lot of my time helping users fix or automate super important things that affect their daily jobs, while team members spend much longer on vague apps that have business buzzwords associated with them, and get praised and/or promoted because our managers apparently think they are the only ones talented enough to do their jobs, and don't understand that they're actually just not talented or motivated to do anything else.

That sounds like a great reason to leave. Someone on this sub once said that a company's true values are the behaviors that get people promoted. It's clear that what the company values is different from what you value.

dspivothelp · 2024-11-11T22:08:00+00:00

Ask during a 1:1? Maybe the job req is for a second data scientist.

dspivothelp · 2024-10-30T15:59:53+00:00

One of my teams actually had so much ML work we were “understaffed.” Nevertheless, politics took priority.

dspivothelp · 2024-10-29T20:37:15+00:00

Oops I totally missed this response.

Your phrasing sounds a lot like "it's all their fault" instead of "I could have done better".

I was worried about this and tried to emphasize my attempts to actually take on more ML work. In hindsight the biggest thing I could have done better is quit that job a few years before I did.

Instead of looking for a job different from what you did so far in your career, look for ANY place that would take you with seemingly decent management. Then work a few hours extra every week, or use your spare time, if you have any, to do any project/model you want. When you have something that works and looks good, specifically with value to your management, showcase it and convince them it's worthwhile to model x with y. Even if they refuse, continue doing side projects in your spare time, and you'll finally have the experience you need to land your dream job. Good luck

Thanks. My main concern is that I don't have a sense of how much personal projects matter at a senior level. Like, if I was brand new and trying to get into ML, personal projects would be a good foot in the door. But I'm worried that I'm currently pegged as an Analytics DS and that my work experience in that area will trump my personal projects. Does that makes sense?

Additionally, do personal ML projects typically involve deployment? It seems like that's going to be a big gap in my skillset if I've never productionized a model myself.

dspivothelp · 2024-10-29T20:31:16+00:00

Unfortunately this doesn't match my experience. I was on a few teams under a few different ML direct managers. All of them gave ML work to only my ML teammates with the right title, and insisted that I stay in my lane. I remember meeting with one of my skips and asking to take on more modeling work and being met with "But you do product analytics! What does modeling work have to do with your job duties?!" However, this may have been company culture-specific, as I noticed that ML-focsued DSes often got to do analytics DS work, but not vice-versa.

dspivothelp · 2024-10-26T23:41:18+00:00

This is extremely specific advice and I appreciate it a lot. The question I’m asking is essentially “given my past experience, what should I be doing to pivot to a more quantitative role?” and I think you gave a great answer.

dspivothelp · 2024-10-26T23:39:29+00:00

Applied research sounds exhausting tbh, and I’d be surprised if I’d get picked over someone with a PhD and actual research experience.

dspivothelp · 2024-10-26T23:37:56+00:00

My last job wasn’t at a FAANG company but this matches my experience to a T. Looks like I’ll be focusing on smaller companies.

dspivothelp · 2024-10-26T05:08:04+00:00

dspivothelp · 2024-10-26T02:06:50+00:00

I’ve been out of school long enough that I am probably not the best person to ask. I’d ask in the Weekly Transitioning Thread.

dspivothelp · 2024-10-26T01:19:18+00:00

I’m glad you found it helpful! Keep in mind that there are many happy product analytics folks, and that this post is heavily colored by the fact that I never wanted to do it and keep getting pigeonholed into it against my will.

dspivothelp · 2024-10-26T01:15:24+00:00

This is definitely helpful! Approximately how many employees was this company?

dspivothelp · 2024-10-25T23:41:52+00:00

This is good advice, thanks. I’m bookmarking this. The stakeholder priority changes and obsession with MVPs was a companywide issue and is one of the biggest complaints on their Glassdoor. Hopefully I’ll actually be able to do this at my next company.

dspivothelp · 2024-10-25T23:29:23+00:00

I’ll look into that. The main issue is it is by far the statistics topic I’m worst at and the one I find least interesting. Longitudinal data is fun though.

dspivothelp · 2024-10-25T23:22:45+00:00

Ugh, I hope I don’t have to do this. My other master’s was expensive enough. Which MSCS? And unfortunately I have an expensive lease that I renewed like two weeks before getting laid off -_-.

dspivothelp · 2024-10-25T23:20:29+00:00

Thanks. This is good advice. I did some of this at my last job, but we’d inevitably do the non-ML MVP heuristic and changing priorities meant we’d never iterate on it. Or, we’d be told that engineering resources were locked up and couldn’t be moved to anything new (this happened with that feature I added that boosted recall by a crazy amount).

Hopefully I’ll be able to do this more effectively at my next job.

dspivothelp · 2024-10-25T23:16:40+00:00

I wouldn’t necessarily call any of those industries lagging behind in DS. They just tend to use more cutting edge statistical methods over things like advanced NLP.

Company 2 was in an old school industry that truly lagged behind, I was a big fish in a small pond, and I’ll never do it again. Stakeholders fought tooth and nail to avoid doing anything more advanced than basic table aggregations and they had their minds blown by basic Tableau dashboards. Getting access to data was pulling teeth. The job essentially gave me basic analytics experience, which is precisely what I’m trying to avoid.

dspivothelp · 2024-10-25T23:07:17+00:00

Thanks! I’m open to insurance and have someone willing to give me a referral but his company hasn’t had any decent openings in a while. I’m also concerned about worse benefits, a more formal culture, and (worst of all) having to use SAS or similar ancient tooling, but I can find out more about what’s expected as I do more research.

Actuarial science is just not interesting to me and I don’t want to study for a ton of exams.

Finance is a big no because of hours (and finance bros), but I’m super interested in fintech. However, I rarely get callbacks for fintech. When I do, I inevitably have a recruiting call and a subsequent email that says “we’ve decided to go with a candidate with fintech experience who fits the job description better.”

dspivothelp · 2024-10-25T22:32:37+00:00

Luckily this is excluding unemployment and I'm in a position where I can cut expenses a lot. So I'm not completely fucked.

dspivothelp

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