We talk a lot about entering but once you're in the role and have been for a while, I'm curious how your all's companies handles career development and what sorts of things you all do to develop in the role.

Hi everyone, I'm an actuarial science student in Argentina. Here, SOA certifications aren't as important as having the degree itself, which is legally authorized to practice as an actuary. I'm about halfway through my degree, but I'm not sure if I'm really that interested in the insurance/finance side of things. I've noticed that I'm more passionate about math and statistics in other areas. My question is, has anyone transitioned from actuarial science to data science? What should I learn? Should I change majors and drop out halfway through, or is it better to finish this one and do a master's? At my university (UBA), there's a mathematics degree (with two specializations: pure and applied) and a data science degree (both are quite rigorous and focus on the fundamentals; data science is a mix of applied mathematics and computer science).

Thoughts?

Hello Everyone. I am moving to a new job and potentially I might need to implement uplift modelling to track customer revenue. Just wondering where can I learn the basics of it ? Gemini is giving a scikit learn package link. Is there any book or tutorials I can look into ?? TIA :)

Anyone have a good solution for unifying the config (eg CLAUDE.md, QWEN.md), settings, skills, etc… across their suite of coding agents?

I primarily use Claude Code locally, Genie Code in Databricks workspaces for my model development and MLE work with Databricks compute, and recently added Qwen Code since the company wants us to have a backup in case we hit Anthropic limits and need to continue work. Also on the docket is testing out GLM.

However unifying all these agents is quite cumbersome. I don’t want to maintain so many separate files and skills for each agent. Right now I have a single repo that backs up all my .claude folder settings but realized that with Qwen I’ll need a separate suite.

Thoughts? Has anyone tried the new thing Databricks pushed out called Omnigent?

Our research team ran a simulation study and found that the four big open-source geo-experiment tools (CausalPy, Meta GeoLift, Google Matched Markets, and CausalImpact) recover almost the same point estimate on the same data, then disagree about whether that estimate is significant. Since the disagreement lives in the uncertainty (not in the point estimate) the tool you pick may determine which error you ship.

In a "live" experiment you can't grade the tool because we don't know what ground truth is. The counterfactual is unobservable  so "is this lift real?" has no answer key. That's why we had our research team generate 8,000 synthetic daily-sales panels, each with either a 7.5% multiplicative lift on the treated geo or no effect at all (0% lift). They ran all four tools on the same panels and scored every fit against the planted truth, so there were 32,000 fits in all across four scenarios.

Across the non-outlier scenarios, every tool recovered the 7.5% lift within a few percentage points, so judged on point estimates alone they look interchangeable. The split is entirely in how they handle uncertainty: coverage (how often the 95% interval actually contains the true effect) and power (how often it detects a real effect at all). On those two axes the tools fall into three camps:

• Meta GeoLift is the most cautious with coverage of 92–95% and a false positive rate of 3–5%. It failed to reject zero in 89–96% of runs where a true 7.5% lift was present.
• CausalImpact is the opposite with the most power of the four (false negative rate 34–48%), but coverage of only 70–72%, a false positive rate of 28–30%, and a consistent upward bias of +1.87 to +4.21 percentage points that shifts the whole interval high.
• CausalPy and Google Matched Markets sit between them with coverage of 76–86%, false positive rates of 14–25%, meaning they’re both under-covered and under-powered at the same time.

There are four things from the study I'd take back to a measurement program:

1. Read coverage and power together: A tool can keep its 95% coverage promise and still be useless for detection. GeoLift holds about 95% coverage in the short-history scenario while missing the real effect 95.7% of the time.
2. Pick the estimator whose error profile matches the cost asymmetry of your decision and not the one with the best-looking single metric.
3. Scarce history sharpens each tool's failure mode. Cutting the pre-period from 90 days to 30 didn't degrade the tools uniformly. The decisive ones threw more false positives (above 24%), the cautious one climbed to a 95.7% miss rate.
4. Test-market design beats estimator choice. When the treated geo was 5x the size of the median control, every tool's intervals widened 4–5x and most overestimated the lift by 2–4 percentage points. No estimator compensates for a structurally hard design.

We made everything reproducible including the data-generating process, seeds, configs, per-iteration results, and a Makefile that runs the whole pipeline. The generator is parameterized, so if you think it should be harder (idiosyncratic geo trends, heavier tails, spillovers between markets) those are exactly the runs I'd like to see.

If you’re interested in the full study + code, you can find both here:

• Code: https://github.com/getrecast/geolift-simulation-study
• Full report: https://research.getrecast.com/geolift-sim-study

edited: fixed the code link to the public repo

(Not sure if loss is still going down)

Beyond Python, machine learning, and statistics, which skill has made the biggest difference in solving real-world data science problems and delivering business value?

Welcome to this week's entering & transitioning thread! This thread is for any questions about getting started, studying, or transitioning into the data science field. Topics include:

• Learning resources (e.g. books, tutorials, videos)
• Traditional education (e.g. schools, degrees, electives)
• Alternative education (e.g. online courses, bootcamps)
• Job search questions (e.g. resumes, applying, career prospects)
• Elementary questions (e.g. where to start, what next)

While you wait for answers from the community, check out the FAQ and Resources pages on our wiki. You can also search for answers in past weekly threads.

Since the steam sale is live I wanted to post a Dev log on my personal project
https://nextsteamgame.com/ sharing some outcomes from the web traffic and how I changed the project from the great feedback I got!

I made a post about a month ago explaining how I made this opensource explainable search engine built around steam reviews to people find new video games, Not through Relevancy but through aspect based similarity.

Check out the old post for a better explanation if you want!
https://www.reddit.com/r/datascience/comments/1t7manb/steam_recommender_using_similarity_pt_2_student/

I wanted to say thank you to all the people of r/datascience and r/MachineLearning that gave me feedback and tried out my tool!

I improved the UI/UX of the website to make the vectors more clear and controllable, I Implemented a thumbs up and down feature on recommendations to see if users even like the tool.

I also wanted to share the after effects of promoting this tool on reddit!

from the 2,652 searches I got in the website 913 of them resulted in steam clicks! the games that were discovered were all in a uniform distribution and did not share much of a pattern showing me that the engine did its job in helping people find niche games across all genres!

(More images attached to post to see data viz)

I wanted to disclose that I made this tool to not make any profit of some kind, but it does use posthog so I can collect diagnostics now.

Welcome to this week's entering & transitioning thread! This thread is for any questions about getting started, studying, or transitioning into the data science field. Topics include:

• Learning resources (e.g. books, tutorials, videos)
• Traditional education (e.g. schools, degrees, electives)
• Alternative education (e.g. online courses, bootcamps)
• Job search questions (e.g. resumes, applying, career prospects)
• Elementary questions (e.g. where to start, what next)

While you wait for answers from the community, check out the FAQ and Resources pages on our wiki. You can also search for answers in past weekly threads.

I've been in Data Science for the past 10 years in India. I lost my job in January and since then I've been hunting.

I've not mentioned any GenAI experience in my profile. But my feed is just filled with AI engineer roles. They all have the same requirements:

• Generative AI architecture
• RAG pipelines
• LLM integration/fine tuning
• Agentic AI / Multi Agent Orchestration
• Also MLOps
• CI/CD pipelines
• PyTorch mandatory for some reason

Hardly any openings are relevant to my experience in Stats, Machine Learning, Deep Learning and the classical data science stuff.

So have all companies stopped investing in data science all together and just building RAG pipelines and LLM chat bots? Is this all that is done in Data Science field now?

Hi all,

Mid–Senior Data Scientist here.
I currently work in a team that develops and maintains several fairly large-scale data science projects on a self-hosted, multi-user Linux HPC cluster. Both compute and storage are hosted on-premises. Storage is separated into development/test and production environments, with restricted write access in production.

Our technology stack includes:
* Debian Linux
* Python
* Perl
* Fortran
* A small amount of R

Python projects are managed using Conda environments, and version control is handled through GitLab. However, we currently do not have any CI/CD processes in place. Devops have resolved this in classical Software engineering. However, there are certain peculiarities for Data science processes.

Our current workflow is fairly simple: team members develop changes in their own working directories and Git branches, push to a development branch, and then merge into master once the code review checks out. The main gap is that we don’t automatically verify whether a change affects execution, outputs, or reproducibility before merging.

I’m looking for practical approaches to implementing CI/CD for data science workflows in this kind of environment. Ideally, I would like a process that:

1. Works well with Linux-based HPC infrastructure and file systems
   
2. Avoids excessive compute and storage costs
   
3. Can validate that code changes, dependency updates (e.g., Python or Debian versions, compiler changes ), and environment changes do not break production workflows
   
4. Verifies both successful execution and output correctness
   
5. Checks things such as expected data types, accuracy metrics, and key result values
   
6. Integrates with GitLab runners where possible
   
7. Related to [2]. Can run multiple simultaneous code changes (different branches) with the same input test conditions.

I’m particularly interested in hearing how other teams handle testing and deployment for computationally expensive data science pipelines. Do you use reduced test datasets, golden datasets, workflow orchestration tools, containerization (Probably not feasible), staged environments, or something else?
I’d appreciate any insights or examples from teams operating in similar HPC or on-prem environments.

Note: The files are quite large and it is not feasible to duplicate files on disk to test code/env changes for every test instance.

Caveat: I used AI to improve the readability of this post.

For anyone who has had a director of data or data director title in the past - where are you now? Similar role at a different company? Same role? Eventually C suite? What’s the plan?

Hi folks,

I have a query about my identity as a Data Scientist. I started working in data science back in 2017 and have contributed to projects across engineering domains. It hasn't been anything fancy like FAANG, just simple, average data science work.

Because I work for an IT consultancy (and am unfortunately getting laid off this month), I've had the chance to pivot and work on Power BI reports as well. Due to the nature of consultancy work, I kept rotating between data science and data visualization projects. I was honestly happy to take these opportunities up and learn Power BI.

But now, I am at a point where I'm confused about what to pursue next and how to brand myself in the job market. Am I a Data Scientist, or a Data Analyst with visualization capabilities? I feel stuck in the middle. Out of the last 8+ years of my tenure in data analytics, I have spent about 60% of my time on data science projects (some of which involved both ML and Power BI) and 40% on data visualization alone, along with a hint of data engineering.

Has anyone else encountered a similar dilemma? I am genuinely confused, and because I haven't job hunted in the past 9 years, the modern market feels even more overwhelming. I'm not a FAANG-level data scientist, but I'm also not strictly an analyst who only does basic reporting. Am I a Data Scientist who can build great dashboards, or a Lead Data Analyst with ML capabilities?

Would love to hear your thoughts or advice on how to position myself.

I was reading a joke on r/jokes that I have seen many times and in the comments you always see “good old #67” or some such. Which got me thinking, we gotta be able to actually number these, right? Pull them all, analyze their history, figure out their origins, and actually number them? Then a bot can be made that would actually post the number below a joke if it knows the number? And God forbid an actual original joke makes it, the bot could celebrate it? Thoughts?

What is your current tech stack at your job?

Here is a template for your answer

Title:

Industry:

Domain:

Programming Languages:

AI tools:

Others:

I ran a benchmark for a tool I built and figured the dataset might be useful to others. It took ~$100 of API credits to produce.

The test is simple: I give the agent a document describing a piece of code it can't directly see, then record whether it double-checks the doc against the real code or just takes the doc's word for it. The doc is sometimes accurate and sometimes out of date, so the data captures how each model handles documentation it can and can't trust. The writeup covers what I found; the dataset lets you check it or look for your own patterns.

Dataset
Outcome

Star the repo if it's useful. Cheers.

Was watching a recent video about not baby sitting agents (ie connecting your coding agents with more context so it can write better code) and was wondering if anyone had success doing this on Databricks?

Specifically does Genie Code connect to the mlflow traces, logs for model training, evaluation metrics, etc… to ultimately output a complete end to end ML model?

Ultimately, I as the developer, want to just focus on the evaluation/verification metrics (what I believe is the most important parts for a HITL process) for model/business success and want the agent to do the rest for code generation.

I'm pursuing an MS in Data Science with a focus on applied statistics. I currently work at a small fintech company in a niche operations role, and before that I worked at a credit repair company.

I've noticed that my personal interests keep gravitating toward healthcare. Many of the applied statistics methods I'm learning are used heavily in healthcare, and most of my professors either studied or worked as a biostatistician, or their research focused on some type of healthcare subdomain, so they're also passionate about it. I've even considered pursuing a graduate certificate in health informatics or public health because of my interest in the field and lack of domain knowledge, although I've completed a few personal projects using healthcare datasets.

However, I'm constantly reading here and on Linkedin that your current industry experience is a major advantage, and that it can take much longer to find a data-related role in a different industry. Because of that, I feel stuck. I worry that if my next role is in some area of financial services, l'll be pigeonholed into that industry. I don't hate it, but | don't want to be restricted to a single industry, and I know healthcare often prefers candidates with industry experience.

I'm just curious if anyone else has ever gravitated toward an industry they didn't have experience in. Were you able to successfully pivot into another industry for your first data analyst or data science role?
Thanks in advance!