Job Description
About The Role
The role focuses on translating messy, high-volume business data into models and analyses that drive product and strategy decisions - causal inference, forecasting, experimentation, and ML-driven insights that ship to production.
Working closely with engineering, product, and leadership, the data scientist owns problems end-to-end: framing the question, designing the analysis or model, and landing results in systems stakeholders actually use.
Key Responsibilities
The role focuses on translating messy, high-volume business data into models and analyses that drive product and strategy decisions - causal inference, forecasting, experimentation, and ML-driven insights that ship to production.
Working closely with engineering, product, and leadership, the data scientist owns problems end-to-end: framing the question, designing the analysis or model, and landing results in systems stakeholders actually use.
Key Responsibilities
- Design and analyze A/B tests and quasi-experiments (diff-in-diff, synthetic control, CUPED) to measure the causal impact of product changes
- Build, validate, and deploy forecasting and classification models using Python, scikit-learn, and XGBoost, with monitoring for drift and degradation
- Develop ETL and feature pipelines in SQL and PySpark, partnering with data engineers to ensure data quality and reproducibility
- Partner with ML engineers to productionize models via Docker, Airflow, and cloud platforms (AWS or GCP)
- Construct dashboards and self-serve reporting in Tableau or Looker that leadership teams actually use for decision-making
- Communicate findings clearly to technical and non-technical audiences, including written analyses and exec-level presentations
- Contribute to team standards around experiment design, statistical rigor, and documentation
- 3–6 years of experience as a data scientist or in a closely related analytical/ML role
- Expert-level SQL and strong Python (pandas, numpy, scikit-learn); comfort working with large-scale datasets
- Demonstrated experience designing and analyzing controlled experiments and applying causal inference methods
- Hands-on experience building and shipping ML models to production, not just prototyping in notebooks
- MS or PhD in statistics, applied math, computer science, economics, or equivalent practical experience
- Strong written and verbal communication - able to defend methodology and tradeoffs to skeptical audiences
- Bonus: experience with big data tooling (Spark, dbt, Snowflake/Databricks), uplift modeling, bandit algorithms, or publishing/applied research experience
Most job openings for the size of the industry in September: professional and business services, 5.5 for every 100 jobs. Fewest: construction, 3.4. Compare industries →
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