Data Science Manager - Pricing

About the RoleWe are looking for a "Data Science Manager - Pricing" to lead the team responsible for the operational excellence of HP’s worldwide machine learning-based pricing model. This model determines optimal prices for hundreds of thousands of products every day, driving critical business decisions across global markets.Key ResponsibilitiesAs the Data Science Manager, you will oversee a team focused on ensuring the pricing model remains accurate, reliable, and continuously optimized and aligned with business goals. Your team’s core responsibilities include:
Continuous Model Retraining: Maintain agility by retraining models to quickly reflect market and competitive dynamics.
Cross-Functional Collaboration: Act as the primary liaison between data science, engineering, and business teams to align pricing strategies with organizational goals.
A/B Testing of Pricing Strategies: Design and execute experiments to evaluate and improve pricing strategies.
Model Monitoring & Reliability: Ensure all business processes dependent on the pricing model run smoothly, with proactive monitoring and issue resolution.
Incident Management & Risk Mitigation: Establish protocols for rapid response to model failures or anomalies to minimize business impact.
Talent Development: Mentor team members, foster a culture of learning, and support career growth within the team.
What We’re Looking For
Strong background in data science, machine learning, or applied statistics, ideally with experience in pricing or revenue optimization for B2B businesses.
Proven ability to work in complex, global environments and manage operational processes for ML systems.
Leadership experience: Previous people management experience is a plus, but not mandatory. We welcome candidates who have served as team leads and are ready to transition into a full managerial role.
Excellent communication and stakeholder management skills.
Preferred Qualifications
Strong software engineering background, with proven experience in productionizing and scaling global ML models.
Experience with ML model lifecycle management (training, deployment, monitoring).
Familiarity with A/B testing frameworks and experimental design.