The B2B Pricing Data Science team at HP is seeking a Senior Data Scientist to build and maintain analytics and ML systems for global pricing strategies. You will work at the intersection of econometrics and production data science to improve causal inference models and design experiments for pricing interventions. Responsibilities: Advance causal inference components including price elasticity estimation, uncertainty quantification, and optimization logic. Design and analyze experiments and quasi-experiments such as A/B tests, diff-in-diff, and synthetic control. Translate methodologies into production-ready Python code following engineering standards. Support operational health through incident management, model monitoring, and retraining cycles. Communicate findings to global stakeholders. Requirements: 4–5+ years of professional experience in data science, econometrics, or quantitative research. Strong grounding in causal inference, potential outcomes framework, DML/orthogonalization, CATE estimation, instrumental variables, and panel methods. Solid applied ML skills including gradient boosting and regularized regression. Proficient in Python for production environments, including scikit-learn and Git-based workflows. Hands-on experience with Databricks or similar cloud platforms. Proactive, self-directed working style with fluent English communication skills. Preferred Qualifications: Experience with causal ML libraries like EconML or DoWhy, and conformal prediction. Familiarity with GenAI coding assistants. B2B pricing domain knowledge. Advanced degree (MSc/PhD) in economics, statistics, or a quantitative field.
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A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
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01
Understand the role
What matters most
Advancing Causal Inference Models
The role requires enhancing the core pricing algorithm using advanced econometrics. You must demonstrate expertise in Double/Debiased Machine Learning (DML), heterogeneous treatment effects (CATE), and instrumental variables to refine price elasticity estimation and uncertainty quantification.
Production-Grade Engineering
Methodological improvements must be translated into high-quality, maintainable Python code. You are expected to follow rigorous engineering standards, including writing unit tests, documentation, and utilizing Git-based workflows within a Databricks environment.
Experimental Design and Analysis
You will be responsible for designing and interpreting experiments, including A/B tests and quasi-experimental methods like diff-in-diff and synthetic control. This requires the ability to measure pricing interventions even when clean randomization is not feasible.
Operational System Maintenance
Beyond model development, you must ensure the health of production systems. This involves proactive incident management, continuous model monitoring, and executing periodic retraining cycles to maintain the accuracy of the global pricing engine.
Likely challenges to think through
Bridging Econometrics and Production ML
A significant challenge is reconciling theoretical econometric models with the constraints of a live production environment. Prepare by being ready to discuss how you balance model complexity with latency requirements and how you ensure causal estimates remain robust during automated retraining.
Communication Across Distributed Teams
The role requires collaborating with stakeholders across time zones, specifically CET-based colleagues. Prepare by highlighting your experience in over-communicating technical findings to non-technical business stakeholders and your ability to work autonomously while maintaining alignment in a global team.
Navigating Non-Randomized Data
Pricing in B2B often lacks the luxury of perfect A/B testing. You will likely face scenarios where you must rely on quasi-experimental designs. Prepare by reviewing your experience with synthetic control or instrumental variables and be ready to explain how you validate causal claims when data is observational.
03
Practice your answers
Technical practice
Double Machine Learning (DML) for Price Elasticity
What to coverExplain how DML addresses omitted variable bias in price elasticity estimation. Discuss the role of orthogonalization in isolating the treatment effect from confounding variables and how it improves upon standard regression models.
Experimental Design in Non-Randomized Settings
What to coverDescribe how you would use synthetic control or difference-in-differences to measure pricing intervention impacts when clean A/B testing is not feasible. Focus on the assumptions required for these methods and how you validate them.
Productionizing Causal Models
What to coverDiscuss the challenges of deploying causal inference models into a production environment. Address how you ensure code maintainability, handle model monitoring, and manage retraining cycles for models that influence live pricing decisions.
Heterogeneous Treatment Effects (CATE)
What to coverExplain the importance of Conditional Average Treatment Effects in B2B pricing. Describe how you would use causal ML libraries like EconML to estimate how price sensitivity varies across different customer segments or product categories.
Uncertainty Quantification in Pricing
What to coverHow do you incorporate uncertainty quantification into your pricing optimization logic? Discuss the use of conformal prediction or other methods to provide confidence intervals for your estimates and how these influence business risk management.
Predictive vs. Causal Modeling
What to coverProvide an example of a scenario where a high-performing predictive model (e.g., gradient boosting) fails to answer a causal question. Explain the statistical judgment required to distinguish between correlation and causation in pricing data.
Instrumental Variables in Pricing
What to coverWhen would you employ instrumental variables in a pricing context? Describe the criteria for a valid instrument and the potential pitfalls if the instrument is weak or violates the exclusion restriction.
Databricks and Cloud Data Workflows
What to coverDescribe your experience using Databricks for large-scale data processing and model deployment. Focus on how you manage Git-based workflows and CI/CD pipelines within a cloud environment to ensure production-ready code.
Behavioural practice
Communicating Complex Methodology to Stakeholders
What to coverDescribe a time you had to explain a complex causal inference finding to a non-technical business stakeholder. How did you translate the methodology into actionable business recommendations while maintaining transparency about model limitations?
Handling Ambiguity in Research
What to coverShare an example of a project where you had to operate with a high degree of autonomy to solve a pricing or research problem. How did you define your own scope, manage roadblocks, and ensure your work aligned with broader team goals?
Collaborating in a Distributed Team
What to coverGiven the global nature of this role, describe your approach to over-communicating and collaborating effectively across different time zones. Provide an example of how you proactively managed a project involving remote partners.
Incident Management and Operational Health
What to coverTell me about a time you had to troubleshoot a production model failure or unexpected performance drop. What was your process for identifying the root cause, communicating the impact, and implementing a fix?
02
Sharpen your knowledge
Topics to prepare
Causal Inference and Potential Outcomes
Review the potential outcomes framework, instrumental variables, and panel methods. Exercise: Define a scenario where a standard predictive model fails to capture causality and explain how you would apply DML or orthogonalization to isolate the treatment effect. This is critical for the role's focus on price elasticity estimation.
Experimental Design and Quasi-Experiments
Study methodologies for measuring interventions when randomization is limited, specifically diff-in-diff and synthetic control. Exercise: Design a quasi-experimental study to evaluate a B2B pricing change in a market where A/B testing is not feasible. This directly addresses the requirement to measure pricing interventions.
Skills to revise
Production-Ready Python Engineering
Focus on writing clean, testable, and modular code using scikit-learn and Git. Exercise: Refactor a prototype script into a production-ready module with unit tests and documentation. This aligns with the team's requirement for shipping maintainable code.
Causal ML Libraries (EconML/DoWhy)
Gain hands-on familiarity with EconML or DoWhy for CATE estimation. Exercise: Use a sample dataset to estimate heterogeneous treatment effects using a Double Machine Learning estimator. This is a preferred qualification for the role's advanced causal modeling tasks.
Databricks and Cloud Deployment
Practice developing and deploying ML workflows within a Databricks environment. Exercise: Create a notebook that handles data ingestion, model training, and monitoring within a cloud-based cluster. This is essential for the operational health and deployment responsibilities of the role.
04
Your preparation checklist
Prepare a Causal Inference Case Study
Develop a concise walkthrough of a past project where you estimated a causal effect, such as price elasticity or a treatment effect. Be prepared to explain your choice of methodology (e.g., DML, instrumental variables, or synthetic control), how you handled potential bias, and how you validated the results.
Demonstrate Production-Ready Python Standards
Review your GitHub repositories to ensure your code reflects production standards: modular functions, comprehensive unit tests, clear documentation, and clean Git commit history. Be ready to discuss how you integrate these practices into a Databricks or cloud-based deployment workflow.
Refine Experimental Design Expertise
Prepare to discuss scenarios where clean A/B testing is not feasible. Practice explaining how you would apply quasi-experimental methods like diff-in-diff or panel methods to measure the impact of pricing interventions in complex B2B environments.
Formulate Strategic Questions for the Team
Prepare thoughtful questions regarding the team's current pricing engine architecture, how they balance model retraining cycles with operational stability, and how they currently leverage GenAI coding assistants to accelerate their development lifecycle.
Review Causal ML Libraries
Refresh your knowledge of libraries like EconML or DoWhy. If you have used them, prepare a brief summary of how they helped solve a specific problem; if not, familiarize yourself with their documentation to demonstrate your ability to adopt these tools for CATE estimation.
Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
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