Over the past several months, our research team pre-trained and empirically audited a 1,189,135,740-parameter Financial Conformer Foundation Model across 14 calendar years (2010–2023; 15,030 steps) of 1-minute US equity bars across 15 liquid assets (AAPL, NVDA, SPY, QQQ, etc.).

Instead of cherry-picking positive backtests, we subjected the model to 35 hypothesis tests, 1-bar execution delay, and state-dependent turnover:

1. The Scale Saturation Limit: Scaling from a 63-parameter linear Ridge regression to 1.189B parameters yields no consistent out-of-sample advantage on 1m OHLCV bars (mixed sign across horizons, reversing direction across years). The bottleneck in intraday price prediction is information content (low SNR), not parameter capacity.

2. Adam Optimizer Diffusion: Decomposing walk-forward online fine-tuning shows tokenizer boundary updates sit at 97.9% of the theoretical Adam Brownian noise ceiling (matching shuffled-label controls), while deep backbone weights are heavily damped by second-moment normalization, producing zero statistically significant forward-transfer gains (p >= 0.50).

3. Execution Friction Reality: Apparent long-horizon directional predictability is predominantly prompt 1-minute order-flow continuation. Introducing a 1-bar execution delay cuts 2024 net Sharpe from +2.04 to +0.48, and at 1.5 bp execution fee, out-of-sample returns collapse to -0.99 in 2025 and -1.67 in 2026 (DSR <= 0.028).

All model weights, audit logs, and papers are open-sourced:

• Model Hub (weights & checkpoints): https://huggingface.co/Ultron28/ohlcv-transformer
• Code & Telemetry: https://github.com/AirBorneAI/ohlcv-transformer
• CERN Zenodo DOI: https://doi.org/10.5281/zenodo.23466965

Happy to discuss architecture specifications, pre-training dynamics, or empirical backtest methodology!

I’m a junior data scientist and I have 1 yoe at a medium sized company. Recently my job has made me so anxious I cannot even sleep. Basically, I deployed a model for the first time, and I cited great improvements, but due to holes in my QA, the QA person did not find that the model actually improved that much in production.

Im already anxious enough, but my manager made me even more anxious. He got pretty pissed that this happened.. so he proceeded to publicly shamed me in standup, telling my team of 6 that they should learn from this lesson. In our 1-1, he told me that if our director sees this, it’ll be bad for my reputation, especially if our clients complain about this.

So I asked him if I could get a second pair of eyes on my work. This is because barely anyone helped me with this project. Only one other person helps me with deployment, but they don’t know anything about how this model works. My manager agreed, but he also said that even if the second person looks over my work, they wouldn’t really know what’s happening either. So it’s all up to me to catch stuff like this. And we ended the meeting with no concrete solution.

I get that it’s important to not make mistakes in production, but is it normal to be this tough on a junior? I literally feel so shit right now. I just feel so incapable and I can’t believe I didn’t catch it the first time. I’m literally considering quitting this field if I have to deal with this for another 40 years….

Edit: he also said "i thought you would have a good handle of this by yourself already, but I guess not" 💀

Edit2: thank you all for your reassurance. I have set up a meeting with the QA person to go over stuff before I deploy again. Hopefully I can learn something from them. Wish me luck for the next deployment 🤞

I'm experimenting with agents for data analysis, but I find myself struggling to work with more than one at a time. I'm interested in how other data scientists handle the human review part.

Thinking of a recent analysis, what did you let the agent do, and what did you check yourself before trusting the result? A concrete example of a check that caught a problem, or convinced you the output was usable, would be helpful.

If you've found a repeatable workflow, I'd also like to hear how you split up the work and whether the review effort got smaller over time.

In marketplace experiments, the most actionable metrics are often the ones with more variance, making them harder to power. This tradeoff is particularly important when using geo-based experiments where you can't necessarily or cheaply add sample.

Suppose Airbnb wants to test highlighting cheaper listings; this should increase bookings while likely reducing AOV. The net effect of a higher conversion rate and reduced AOV depends on elasticities and profit margins. Revenue or contribution margin are the metrics that matter the most (should be the most actionable), but these have far more variance than simple proportions like a conversion rate.

Running the geoX longer helps less than you might expect if you haven't done these before: weekly bookings within the same region are almost certainly correlated so you'd need to cluster the SEs, which can brings the effective sample size "roughly" down to the number of unique geos (if the correlation within geos is high).

So you have a few options: (1) focus on the actionable metric but accept more uncertainty through a higher alpha, ideally combined with (2) variance reduction techniques like CUPED, or (3) power the test on a lower-variance metric like conversion rate and try to bound the AOV loss using product sense and reasonable assumptions.

This comes up frequently on the job and also in senior and staff data science interviews. Real-world business problems can be complex, and there's not always a single correct answer. Trying to memorize answers and metrics can help in junior interviews, but at senior you typically will get probed on teh tradeoffs. .

Is there an official definition of an "AI predictive model"?

Can any model, including a linear regression, be considered artificial intelligence if it's using historical data to make predictions?

Or is AI restricted to neural networks that simulate human speech or generate images, music, or scrape the Internet for information? Like ChatGPT or Claude.

My company is setting up guardrails for AI use and there isn't broad agreement how to define "AI".

I've been in the field for 4 years now, but only at one company. I'm curious to hear how other places do this. My company pairs up a data scientist or 2 and a couple different roles from the business-side, the specifics depend on the project. The strategy is that each brings their respective skills, tech or business, and they make sure the project goes smoothly.

How this has looked in practice is that the business side looks to the tech roles for every technical decision, even ones the business needs to own, and the tech side think they don't have to do anything to learn about the business and they look down at communication to non-technical audiences or stepping even a bit outside the box of that specific role.

The impact of this is a bunch of projects that don't meet what the requesting groups really asked for, everyone being upset, and a whole host of other things. Everyone here is wicked smart, but our operating model and egos are holding us back from being actually helpful.

Is everywhere this dysfunctional? And where should I look for my next role if I want a place that has a strong technical group and understands the science of what we do, but also expects and develops the technical group to work with the business side and be more of a bridge between what the company is trying to do and what data science can provide? I'm looking to move around the 5 year mark if things don't get better here, but with how the job market is right now I feel like I need to start work for that now. I want to make sure I don't end up in more of the same.

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 went back through my last 10 Python coding rounds for product and marketing data science roles, nearly all at Big Tech companies. Unlike SQL rounds, which tend to be fairly standard, these came in 5 distinct formats:

• Recreate a statistical concept as a function (3/10). Pandas, numpy and other standard packages were allowed.
• AI-assisted, open book (3/10). These were often investigative, closer to a diagnostic case study than a coding puzzle.
• Pandas and data manipulation (2/10). Data transformations combined with modeling, either coding up a linear regression or talking through a modeling strategy for the data.
• Interpret code handed to you (1/10). This was also a statistical concept in a function, but I had to interpret the code rather than write it.
• Base Python, LeetCode-style (1/10). Syntax seemed heavily discounted here: I made several syntax mistakes but was clear about the logic and passed.

The obvious caveat is that this is a sample of 10 from one person, so the mix will vary with company, team and role. That said, AI-assisted challenges seem to be on the way in and increasingly likely to be a part of your interview process.

Please, could you share your best resources to learn about RecSys at scale? I have a few already, but I want to know what other resources you are reading. Thanks

Hello lovely community,

Recently i started in a new company that is a heavy user of the Azure ecosystem, they tend to do all their reporting in PBI, need a way to automate the creation of multiple PBI dashboards with different visuals and design, want to use AI to generate .pbix files where i set the business logic, relationships and data normalization but the LLM handles the visuals and meassures, what do you guys use in your day to day to handle this situation?

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.

The images below map the 4 types of data science case study questions I encountered across 80+ interview rounds at 16 companies in April-May 2026. For each type it covers the interviewers' typical opening, what's being tested, and the trap I saw most often in my own interviews and in mock interviews with clients. Identifying the type early matters because the correct approach differs for each one.

A few years ago the case study round was close to synonymous with marketplace experimentation. That question still regularly appears, but the same loop can now hand you (1) a launch evaluation, (2) a root cause analysis of a metric that suddenly moved, (3) an opportunity sizing decision about whether a build is worth its cost, or (4) a targeting problem that turns on heterogeneous treatment effects. Any of the four can also end with questions that call for observational causal inference, depending on your level.

(I created this for my socials, but hopefully it fits the Reddit structure!)

Sharing this blog, that compiles some real world examples of Causal Inference being applied to provide answers to otherwise ‘unanswerable’ questions.

My hope is that the examples make the methods clearer, particularly among those new to the field. Happy reading.https://medium.com/@joshamayo7/what-would-have-happened-otherwise-causal-inference-in-practice-b412d2b4e3f7

Quite a few real world data problems look like this: many related time series, few noisy readings, and long gaps. Local price indices, sensor networks, or small area stats.

I built a small PyTorch model to estimate a smooth underlying level for every location, day, and target even where nothing was observed.

Key ideas:

Mask, don’t drop missing targets in the loss

Weight observations by how many raw readings they average

Share information across locations (area + station offsets) and across related targets (joint heads)

Enforce smoothness with a curvature penalty over the full time grid, including gaps

I tested it on hourly PM2.5/PM10 from 12 Beijing stations, thinned to a sparse panel, and scored against the full data’s 31day moving average.

What I found:

The network fills long gaps and very sparse stations far better than smoothing each series on its own

Modelling both pollutants together recovers a completely dropped series (PM10 at one station) from its PM2.5

But elsewhere, sharing across targets costs a little accuracy vs. modelling each pollutant alone

A simple ±15day mean is hard to beat when data is dense. The network’s smooth time curve misses shared daily shocks

Writeup with code, figures, and results:

https://prteek.github.io/regression_room/posts/beijing_air_quality/

How do you choose between causal inference models when you can never see the true effect? Model selection is well documented in machine learning, but it often gets skipped in causal inference work. Doing it well is one of the things that separates senior and staff-level data scientists from more junior ones.

I simulated two years of weekly bookings for 40 regions, five of which received a treatment on the date marked by the red vertical line. Because I generated the data, I know the ground truth: the treatment increased bookings in those five regions by exactly 10%. With five treated regions and 35 untreated ones, two natural options are synthetic control and difference-in-differences, though there are others.

Code in colab and explanatory video, free and ungated: https://whatstheimpact.com/learn-causal-inference/

When I gave Claude Code free rein on this data, it built its whole analysis around difference-in-differences where it found a 24% increase, more than double the true effect. To its credit, it flagged that the pre-treatment trends weren't parallel, but it never tested whether a different model would be more accurate.

Importantly, Claude skipped an A/A test, sometimes called a placebo test. You take only the data from before the real treatment, pretend the treatment started three months earlier, and randomly picked five regions (for consistency) to call "treated." Because nothing actually happened in that window, the true effect is zero. That means any effect the model reports there is pure estimation error, and I repeated this for 500 draws for each model to see the full spread.

For difference-in-differences, 90% of the placebo estimates landed between -14% and +10%. The RMSE, which measures how far the estimates typically land from the true answer of zero, was 7.6 percentage points. Synthetic control on the exact same placebo draws was much tighter, with 90% of estimates landing between -7% and +9%. Its RMSE dropped to 4.9 points, more than a third lower than difference-in-differences.

The outcome you model is a choice too, so I also ran synthetic control on bookings per 1,000 people instead of raw bookings. Putting every region on the same scale helps the model track the treated regions more closely before treatment, and the RMSE fell a bit further to 4.4 points.

Sure enough, the real treatment period lines up with that ranking: synthetic control estimates 7% on raw bookings and 9.6% per 1,000 people, both much closer to the true 10% than the 24% from difference-in-differences. With one simulated dataset, I wouldn't read much into the small gap between the two synthetic control versions or claim that synthetic control always wins. What carries over is the workflow: in real work you never see the ground truth, but you can usually run this A/A test on whatever models you're considering before trusting one.

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.

Looking to hear about your own company's work practices to compare to my own. I'm much more familiar with Azure, so I'm going to use that terminology, but I assume everything is basically the same for AWS.

Can you go into the Azure Portal and just click "create" for, say, a SQL Server or App Service and configure it through the Portal GUI?

Or do you need to request and configure through configuration files? Can you freely commit architecture config files ("architecture as code") to create the Azure components in Dev?

Or is it even more locked-down and hands-off? Required to submit paperwork requests to an Architecture team?