Senior Data Scientist– Pricing

Senior Data Scientist– Pricing

Description -About the role

HP's B2B Pricing Data Science team builds the analytics and ML systems that set and negotiate prices for commercial PC and Print products, sold both through channel partners and directly to business customers worldwide. Our pricing engine combines machine learning price and win-rate models, causal price elasticity estimation, and constrained optimization, and is deployed in live A/B pilots across multiple markets. We are expanding the team's global footprint to better support a global product.

You will work at the intersection of econometrics and production data science: improving the causal inference core of our pricing algorithm, designing experiments to measure the impact of pricing and process interventions, and shipping your work as production-quality Python code. You will report directly to the team's manager, partner closely with our senior applied data scientist on methodology, and operate with a high degree of autonomy.

What you will do

• Advance the causal inference components of our pricing system: price elasticity estimation (double/debiased ML, heterogeneous treatment effects), uncertainty quantification, and optimization logic.
• Design, run, and analyze experiments and quasi-experiments (A/B tests, diff-in-diff, synthetic control) to measure the impact of pricing and process interventions, including where clean randomization isn't possible.
• Translate methodological improvements into production-ready Python code, with tests, documentation, and adherence to team engineering standards.
• Contribute to the operational health of our production systems: incident support, model monitoring, and periodic retraining and validation cycles.
• Communicate findings and recommendations to technical and business stakeholders across time zones.

What you bring

• 4–5+ years of professional experience in data science, econometrics, or quantitative research, ideally with exposure to pricing, revenue management, or marketplaces.
• Strong grounding in causal inference and experimental design: potential outcomes framework, DML/orthogonalization, CATE estimation, instrumental variables, panel methods.
• Solid applied ML skills (gradient boosting, regularized regression) and the statistical judgment to know when a predictive model answers a causal question — and when it doesn't.
• Proficient Python for production: clean, testable, maintainable code; comfortable with the scikit-learn ecosystem, Git-based workflows, and code review.
• Hands-on experience with Databricks (or comparable cloud data platforms) for development and deployment.
• Proactive, self-directed working style: you reach out early when blocked, over-communicate in a distributed team, and don't wait for co-location to collaborate.