Senior Data Scientist– Pricing
HP Inc. (company site, opens in a new tab)4+ yrsBengaluruOn-siteDirect career pageEst. 20–35 LPA
Posted on 23 Sept
Reach people at HP Inc.
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at HP Inc. you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- 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.
Minimum Requirements
- 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.
Ideal Candidate Profile
- 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.