Staff Data Scientist– Pricing Science
Team: Engineering
Location: Remote - Toronto, Austin, TX, Salt Lake City, UT
Commitment: Full Time
Workplace Type: remote
What You’ll Own
- 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
What you Bring / Requirements
- 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.
- 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).
Who We're Not Looking For
- Someone who only knows how to call .fit() and .predict() without understanding the underlying mechanics.
- Someone who builds black-box models they can't explain, debug, or justify.
- Someone who needs detailed instructions or hand-holding for ambiguous problems.
- Someone who over-engineers solutions when a simple approach would suffice.
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