Staff Data Scientist

As our Staff Data Scientist, you will design and ship production pricing systems such as demand forecasting, price elasticity modeling, dynamic pricing and the experimentation infrastructure needed to measure whether they actually work.
This is a hard, high-stakes problem: your models will directly influence margin and revenue decisions across a portfolio of brands operating at scale. You will own the full arc from framing ambiguous business problems as well-defined ML tasks through to monitoring models that hold up in production.
At six months, success looks like at least one pricing model shipped to production with measurable business impact and an experimentation framework in place that your stakeholders trust. If you have spent time building pricing systems from the ground up, not just consuming them, and you care deeply about rigorous causal inference and honest model evaluation, this role was written for you.

• 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

• 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.

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

The people who do best here are builders. They take ownership, move fast, and want to see the direct impact of their work.

• 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.

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.

Originally posted on Himalayas