Staff Data Scientist– Pricing Science

About the 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 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.

Preferred Qualifications

• Experience in e-commerce, retail, marketplace, or pricing-intensive industries such as airlines, ride-sharing, or fintech.

Why Join

• Portfolio-Level Impact: Your models will influence pricing and margin decisions across a $1B+ portfolio of brands — the output of your work is visible at the executive level from day one.
• AI-First Skill Building: Get hands-on with production ML infrastructure, causal inference at scale, and the Genesis platform — building a modern, applied ML skill set on real retail data problems.
• Ownership: You will own the full problem from framing through production, with the autonomy to make technical decisions and the stakeholder access to see them through.
• 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

1. Recruiter Screen: 30-minute call to cover your background, the role, and logistics.
2. Hiring Manager Interview: Conversation with the Director of Finance and Business Intelligence focused on your pricing science experience, approach to ambiguous ML problems, and how you've driven production impact.
3. Technical / Case Discussion: Deep dive into a pricing or demand forecasting problem — expect questions on model evaluation, causal inference, and production failure modes. Cross-functional stakeholders may join.
4. Executive Interview: Final conversation with senior leadership.
5. Reference Checks: Conducted in parallel with the final stages where possible.
6. Offer: We move quickly for the right candidate.