Staff Data Scientist– Pricing Science

About the role

This role is for a Staff Data Scientist who will design and ship production pricing systems such as demand forecasting, price elasticity modeling, and dynamic pricing. The successful candidate will be responsible for framing ambiguous business problems as well-defined ML tasks, building robust predictive models, and ensuring models are evaluated, validated, and monitored.

Responsibilities

• Design and build production ML systems for pricing, demand forecasting, and related revenue problems
• Frame ambiguous business problems as well-defined ML tasks with clear success criteria and measurable outcomes
• Set the standard for model evaluation, validation, and monitoring — including knowing when CV metrics are misleading and when holdout testing is the only honest answer
• Identify and prevent data leakage, overfitting, and other failure modes before they reach production
• Design and analyze experiments to measure causal impact of pricing decisions
• Debug models that fail in production — understand why they fail, not just that they do
• Translate model limitations, uncertainty, and risk clearly to both technical and non-technical stakeholders
• Partner with product, engineering, and business teams to ensure ML solutions solve real problems

Requirements

• 7+ years of applied ML / data science experience with a track record of production systems that delivered measurable business impact
• Deep experience in pricing, demand forecasting, or revenue optimization — you have built these models end-to-end, not just consumed them
• Expert-level Python and SQL
• Deep understanding of ML fundamentals beyond API-level usage, including model evaluation, validation, and failure mode diagnosis
• Strong grounding in causal inference and experimental design, including the ability to distinguish correlation from causal result
• Ability to work with messy, real-world data and make pragmatic tradeoffs under ambiguity
• Familiarity with cloud ML platforms (GCP/Vertex AI or AWS/SageMaker)
• MS or PhD in Statistics, Computer Science, Operations Research, or a related quantitative field

Qualifications

Preferred qualifications include experience in e-commerce, retail, marketplace, or pricing-intensive industries such as airlines, ride-sharing, or fintech.