Staff Data Scientist– Pricing Science

As our Staff Data Scientist you'll build and ship production pricing systems such as demand forecasting, price elasticity modeling, dynamic pricing, and the experimentation infrastructure that tells you whether any of it is actually working

It's a hard problem with decisions that directly impact margin and revenue across the portfolio. If you've spent time thinking carefully about how to measure price sensitivity at scale, how to run pricing experiments without destroying margin, or how to build forecasting systems that hold up in production, this is your role. 

What You'll Do

• 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
• Build robust predictive models across classification, regression, time series, and causal inference
• 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

Required Qualifications

• 7+ years of applied ML / data science experience with a track record of production systems that delivered measurable business impact.
• Deep pricing, demand forecasting, or revenue optimization experience. You've built these models, not just used them.
• Expert-level Python and SQL.
• Deep understanding of ML fundamentals beyond API-level usage.
• Strong grounding in causal inference and experimental design. You know the difference between a correlation and a result.
• Ability to work with messy, real-world data and make pragmatic tradeoffs.
• 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.
• Self-directed and autonomous. You don't need the problem handed to you fully formed.
• Experience in e-commerce, retail, marketplace, or pricing-intensive industries (airlines, ride-sharing, fintech).

Why Join

The people who do best here are builders. They take ownership, move fast, and want to see the direct impact of their work.
• Executive Access: Work directly with executives and senior leadership to solve real organizational problems and shape people strategy at the portfolio level.
• AI-First Skill Building: Get hands-on with the most advanced AI tools in market — from automation to LLM-powered workflows — and build a modern skill set that sets you apart.
• Accelerated Career Path: High performers are quickly entrusted with greater responsibility, new challenges, and leadership opportunities across our portfolio of brands.
• Competitive Benefits (CAN): Comprehensive benefits including paid time off, RRSP match, group benefits, and employee discounts across portfolio brands.
• Competitive Benefits (US): Comprehensive benefits including paid time off, 401(k) match, medical, dental, vision, supplemental coverage, and employee discounts across portfolio brands.

Interview Process

Step 1 — Recruiter Intro: 30-minute call to cover background, role fit, and logistics.
Step 2 — Hiring Manager Interview: Conversation with the Chief Administrative Officer focused on leadership philosophy and AI-native HR experience.
Step 3 — Technical / Case Discussion: Deep dive into a people ops problem or system design scenario with cross-functional stakeholders.
Step 4 — Executive Interview: Final conversation(s) with senior leadership.
Step 5 — Offer: Reference checks conducted in parallel where possible.