Data Scientist

About The Role

The role sits at the center of the company's decision-making: turning raw product, behavioral, and operational data into models and analyses that shape what gets built next. This is a hands-on data science position focused on causal inference, experimentation, and predictive modeling — not dashboard maintenance.

The data scientist will partner directly with product managers, engineers, and executives, owning analyses and models end-to-end. The work spans designing A/B tests, building forecasting and propensity models, and quantifying the impact of product changes with statistical rigor that survives executive scrutiny.

Key Responsibilities

• Design and analyze A/B tests and quasi-experiments, including power analysis, sequential testing, and CUPED-style variance reduction techniques
• Build and productionize predictive models (propensity, churn, forecasting, LTV) using Python, scikit-learn, XGBoost, and PyTorch
• Construct large-scale data pipelines in SQL and PySpark against Snowflake/Databricks, ensuring reproducibility and data quality for downstream modeling
• Define and instrument metrics frameworks, partnering with product and engineering teams to establish trustworthy KPIs and experiment guardrails
• Apply causal inference methods — difference-in-differences, instrumental variables, uplift modeling — to answer questions experiments can't
• Communicate findings through clear write-ups and presentations that translate statistical results into concrete business recommendations
• Contribute reusable tooling and methodology documentation, raising the analytical standards of the broader data science team

What We Are Looking For

• 3–6 years of experience in data science, applied statistics, or quantitative research, ideally on a product or growth team
• Expert-level SQL and strong Python for data analysis and modeling (pandas, NumPy, scikit-learn, statsmodels)
• Proven track record designing and analyzing controlled experiments at scale, with a deep understanding of statistical validity pitfalls
• Experience with big data tooling such as Spark, Databricks, or Snowflake
• Master's or PhD in Statistics, Economics, Computer Science, or a related quantitative field, or equivalent practical experience
• Strong communication skills with evidence of influencing product and engineering decisions through analysis
• Bonus: experience with uplift/causal ML libraries (econml, causalml), Bayesian methods (PyMC, Stan), publishing applied research, or deploying models in production