Data Scientist

About The Role

The Data Scientist owns the full analytical lifecycle - from framing ambiguous business questions to shipping models and analyses that drive product and pricing decisions at scale.

Working across a modern data stack (SQL, Python, dbt, cloud warehouse), the role blends causal inference, experimentation, and applied ML, partnering closely with product managers, ML engineers, and data engineers to turn data into shipped decisions.

Key Responsibilities

Design and analyze A/B tests and quasi-experimental studies (difference-in-differences, synthetic control) to measure the impact of product and pricing changes Build causal inference models and customer-level propensity/segmentation models using Python (scikit-learn, statsmodels) to inform growth and retention strategy Develop and maintain production-ready features and models, partnering with ML engineers to move models from notebooks to deployment via SageMaker or Vertex AI Write large-scale SQL queries and dbt models against Snowflake/BigQuery to build reliable, self-serve data assets for stakeholders Construct dashboards and metrics frameworks in Looker or Tableau that define how leadership tracks core business health Translate ambiguous business problems into clear analytical plans, hypotheses, and prioritized deliverables Present findings and model results to senior stakeholders, including tradeoffs, confidence levels, and recommended actions What We Are Looking For 3–6 years of experience in data science, analytics, or applied statistics, with at least 2 years at a product-driven tech company Advanced SQL fluency and strong Python skills (pandas, scikit-learn, statsmodels); comfort working in Spark/PySpark environments Hands-on experience designing and analyzing controlled experiments (A/B tests), including power analysis and variance reduction techniques Working knowledge of causal inference methods beyond standard regression: propensity scores, diff-in-diff, instrumental variables Experience with a modern data warehouse (Snowflake, BigQuery, Redshift) and BI tooling (Looker, Tableau) Bachelor's degree in a quantitative field (Statistics, Math, Computer Science, Economics) or equivalent practical experience Bonus: Master's/PhD in a quantitative discipline; experience with causal ML (uplift modeling, CATE estimation); MLOps exposure (Airflow, MLflow); prior experience owning metrics definitions for a product line