Senior Data Scientist– Pricing

Lead development and improvement of causal inference components in pricing systems, including price elasticity estimation and uncertainty quantification.

Design, execute, and analyze experiments (A/B tests, diff-in-diff, synthetic control) to evaluate pricing and process interventions, especially under non-ideal randomization.

Develop and maintain production-quality Python code for models and systems; support operational health through monitoring, retraining, and incident management.

4–5+ years professional experience in data science, econometrics, or quantitative research; ideally with pricing/revenue management/marketplaces exposure.

Strong expertise in causal inference and experimental design methods (e.g., potential outcomes, DML, CATE, instrumental variables).

Proficient in Python production coding with scikit-learn, Git workflows, and code review; experience with Databricks or similar cloud platforms.

Education: Full-time Master’s degree required; PhD is an advantage but not mandatory.

Experienced in deploying causal ML models in production environments integrating econometrics and data science approaches.

Demonstrates autonomous and proactive working style with strong collaboration across distributed teams and global time zones.

Familiarity or interest in B2B pricing domain and usage of causal ML libraries (EconML, DoWhy) and GenAI coding tools enhances fit but not mandatory.