Staff Data Scientist

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

Who We’re Looking For

• 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.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.