$216,000 – $249,500
Listed on Blue Apron’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
A strategic leadership role for an experienced data scientist to shape Grubhub's ML and analytics direction, mentor the team, and drive causal inference and experimentation frameworks across the marketplace. This suits someone with 8+ years in industry who has shipped production ML systems and can balance business impact against technical rigor in complex, noisy environments.
Our summary, not Blue Apron’s wording. The full posting is on their site.
Skills this role names
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What they ask for
Required
- 8+ years industry experience with MS, or 6+ years with PhD in Statistics, Economics, Applied Mathematics, Computer Science, Data Science, Machine Learning, or related quantitative field
- Experience applying data science and machine learning to complex business problems such as marketplace optimization, customer experience, forecasting, personalization, pricing, or product experimentation
- Deep expertise in causal inference, experimentation, and statistical modeling
- Understanding of business and product trade-offs
- Proficiency in Python, data analysis, visualization, and writing production-ready code
- Ability to take data science and ML systems into production with engineering partnership
- Fluency in SQL or similar tools for production-scale datasets
- Experience mentoring and providing technical direction to other scientists, analysts, or engineers
Nice to have
- Experience leading end-to-end design of data science, machine learning, measurement, or experimentation frameworks in marketplace, consumer, fulfillment, logistics, pricing, forecasting, or operations systems
- Experience designing causal measurement strategies for complex systems with multiple interacting layers
- Background in econometrics, Bayesian modeling, experimental design, or observational measurement in high-noise environments
- Experience with applied experimentation including power analysis, heterogeneous treatment effects, guardrail metrics, and interference effects
- Experience building production ML systems combining predictive modeling, causal measurement, experimentation, and business rules
- Influence across product, engineering, operations, business, and data science disciplines
- Experience defining strategy and technical roadmaps for data science, ML, experimentation, or causal inference platforms