Data Scientist (Core ML & Statistics)

What You'll Do:

• Design, run, and analyze A/B tests and other experimental frameworks.
• Apply statistical inference, hypothesis testing, and causal analysis to evaluate product changes.
• Build, tune, and interpret classical ML models (regression, classification, ranking) with a focus on explainability and robustness.
• Use rigorous exploratory analysis to uncover hidden patterns or validate product hypotheses.
• Develop reproducible workflows for experimentation and modeling.
• Collaborate with product and engineering to ensure experiments and analyses translate into well-grounded decisions.
• Contribute to a culture of statistical rigor, clarity, and curiosity.

What We Value:

• Deep understanding of statistics, probability, and causal inference.
• Comfort moving between theory and practice: from modeling assumptions to implementation details.
• Clear, precise communication of methods, assumptions, and results.
• Patience to validate before scaling, and rigor in separating correlation from causation.

Bonus Points:

• Experience with uplift modeling.
• Experience in product analytics or growth-focused DS.
• Experience working with Airflow, BigQuery, dbt, or similar data infra.
• Experience in training and shipping models to production.
• Familiarity with basic experimentation pitfalls (SRM, novelty effects, power issues).

How We Hire:

• Online assessment: technical logic and fundamentals (Math/Calculus, Statistics, Probability, Machine Learning/Deep Learning, Code).
• Technical interview: deep dive into theory and reasoning for fraud detection (no code).
• Cultural interview.
If you are not willing to take an online quiz, do not apply.