Data Scientist

About The Role

The role sits at the intersection of applied statistics, machine learning, and product strategy — building models and analytical systems that directly shape how the business acquires, retains, and serves customers.

This is not a dashboard-only role. The team ships data science to production: experimentation infrastructure, forecasting models, and causal inference pipelines that executives and engineers rely on daily.

Key Responsibilities

• Design and run large-scale A/B tests and quasi-experimental studies (difference-in-differences, synthetic control, CUPED) to measure causal impact of product changes
• Build and deploy predictive models — churn, propensity, forecasting, ranking — using Python, scikit-learn, and gradient boosting frameworks like XGBoost or LightGBM
• Own the full modeling lifecycle: problem framing, data exploration, feature engineering, model selection, and post-deployment monitoring
• Develop and maintain SQL pipelines and dbt models working closely with data engineering to ensure clean, reliable training data
• Translate ambiguous business questions into rigorous analytical frameworks and communicate findings to senior leadership with clear, quantified recommendations
• Contribute to the experimentation platform and statistical methodology standards used across the organization
• Prototype and validate new modeling approaches in notebooks, then productionize them with engineering partners

What We Are Looking For

• 3–6 years of experience in data science, applied statistics, or ML research, with at least one model taken from prototype to production
• Expert-level SQL and strong Python (pandas, NumPy, scikit-learn); comfort working with large datasets in Spark or BigQuery
• Deep grounding in statistics and causal inference: hypothesis testing, power analysis, confidence intervals, observational study design
• Hands-on experience designing and analyzing controlled experiments at scale
• Strong data storytelling skills — able to present technical findings to non-technical stakeholders and influence decisions
• MS or PhD in a quantitative field (statistics, economics, computer science, operations research) or equivalent practical experience
• Bonus: experience with uplift modeling, Bayesian methods (PyMC, Stan), time-series forecasting (Prophet, ARIMA), or building internal experimentation tooling