Senior Staff Data Scientist
Confirmed on the employer's own hiring board on Oct 20, 2026. First seen by Alion on Jul 12, 2026.
Join Wonder Data Science as a Senior Staff Data Scientist, where you will shape the strategic direction of applied data science, mentor other scientists, and collaborate with various teams to enhance our ML and analytics capabilities. You will identify high-leverage opportunities across the business, design frameworks for understanding causal impact, and help define trade-offs that affect customer experience, prediction accuracy, and business impact. Additionally, you will serve as a technical thought leader in data science, mentor a growing team, and partner with engineering to drive architecture decisions.
Missions
- Identifying high-leverage opportunities across the business, including marketplace efficiency, customer experience, ETA accuracy, fulfillment reliability, pricing strategy, supply planning, demand forecasting, and operational performance.
- Designing statistically rigorous frameworks to understand causal impact, separate signal from noise, and guide business strategy through experimentation, measurement, and principled inference.
- Prototyping, experimenting, influencing architecture, and ensuring we operationalize models and insights that actually move business metrics - not just analyses that look good offline.
Profil recherché
- Demonstrated ability to take data science, ML, or causal inference systems into production, partnering with engineering on architecture, deployment, and monitoring best practices- 8+ years of industry experience with MS or 6+ years with PhD in Statistics, Economics, Applied Mathematics, Computer Science, Data Science, Machine Learning, or a related quantitative field
- Experience mentoring and providing technical direction to other scientists, analysts, or engineers
- Fluency in SQL or similar tools for directly interrogating production-scale datasets
- Strong intuition for business and product trade-offs - customer experience vs. efficiency, ETA confidence vs. conversion risk, fulfillment reliability vs. cost, marketplace growth vs. quality, and short-term optimization vs. long-term health
- Proficiency in Python, data analysis, visualization, and writing scalable, production-ready code using object-oriented design
- Deep expertise in causal inference, experimentation, and statistical modeling, including methods such as A/B testing, difference-in-differences, regression discontinuity, instrumental variables, synthetic controls, uplift modeling, or causal impact analysis
- Proven experience applying data science and machine learning to complex business problems, such as marketplace optimization, customer experience, forecasting, personalization, pricing, supply/demand balancing, operational policy changes, or product experimentation
- Experience leading end-to-end design of data science, machine learning, measurement, or experimentation frameworks within marketplace, consumer product, fulfillment, logistics, pricing, forecasting, or operations systems
- Experience with applied experimentation frameworks, including A/B testing, power analysis, heterogeneous treatment effects, guardrail metrics, interference effects, and long-term impact measurement
- Experience designing causal measurement strategies for complex systems where product, marketplace, and operational decisions interact across multiple layers
- Background in causal inference, econometrics, Bayesian modeling, experimental design, or observational measurement in high-noise environments
- Experience building or influencing production ML systems that combine predictive modeling, causal measurement, experimentation, and business rules
- Influence across disciplines - able to align product, engineering, operations, business, and data science around a cohesive ML, experimentation, and measurement strategy
- Experience defining strategy and technical roadmaps for data science, machine learning, experimentation, or causal inference platforms