Data Scientist

ModelingRemote (worldwide)Full time

Every pick the product publishes carries a win probability, and those probabilities are measured against what actually happened. That only works if the numbers are honest. You'll own the models behind them — player prop projections, game outcomes, and the calibration work that keeps a stated 60% actually hit about 60% of the time.

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What you'll do

Build and maintain projection models for player props, spreads, totals, and moneylines.
Own calibration and backtesting, and publish the accuracy numbers the product shows users.
Work from a historical store of box scores and odds snapshots in Postgres and S3, and ship the features your models need.
Find edge by comparing model output against market lines, and quantify it honestly.

What we're looking for

Strong applied statistics: probability calibration, Bayesian methods, and time-series validation.
Fluent Python with the modeling stack (pandas, scikit-learn, and a gradient-boosting library).
Confident SQL against Postgres, and comfort reading large Parquet datasets straight out of S3.
Experience evaluating models against a moving benchmark rather than a static holdout set.
The instinct to distrust a result that looks too good — and the rigor to find out why it does.

Nice to have

You have modeled a betting market, or another market where the price already contains most of the information.
Experience turning a model's uncertainty into something a non-technical user can read and trust.
Published or open-source work on calibration or forecast evaluation.

What you'll work with

Pythonpandasscikit-learnPostgresParquetS3

How hiring works

1. ApplyEmail careers@statsniper.com with the role in the subject line, your resume or GitHub, and a short note on what you'd want to own.

2. Intro callA 30-minute conversation about your background, what you're looking for, and what the role actually involves day to day.

3. Working conversationA working session on a real problem from our domain. No algorithm trivia and no unpaid take-home project.

4. OfferA final conversation with the founding team, then a decision. We aim to go from first email to offer in under two weeks.

Interested?

Email us with Data Scientist in the subject line, your resume or GitHub, and a short note on what you'd want to own. We reply to everyone.

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