Description
HP's B2B Pricing Data Science team builds analytics and ML systems for setting and negotiating prices for commercial PC and Print products. You will work on improving the causal inference core of the pricing algorithm, designing experiments, and shipping production-quality Python code.
Responsibilities:
- Advance the causal inference components of the pricing system
- Design, run, and analyze experiments to measure the impact of pricing and process interventions
- Translate methodological improvements into production-ready Python code
- Contribute to the operational health of production systems
- Communicate findings and recommendations to technical and business stakeholders
Requirements:
- 4-5+ years of professional experience in data science, econometrics, or quantitative research
- Strong grounding in causal inference and experimental design
- Solid applied ML skills and statistical judgment
- Proficient Python for production
- Hands-on experience with Databricks or comparable cloud data platforms
- Proactive, self-directed working style
- Fluent English
Nice to have:
- Experience with EconML, DoWhy, or similar causal ML libraries
- Effective use of GenAI tools for coding and research
- B2B pricing domain knowledge
- Advanced degree in economics, statistics, or a quantitative field