Senior Staff Data Scientist

Grubhub (Wonder) is hiring a Senior Staff Data Scientist in Chicago, IL (hybrid) to build applied data science and machine learning systems that improve marketplace operations and customer outcomes.

Responsibilities

• Act as a technical thought leader for Data Science: define principles, frameworks, and best practices for Wonder’s use of data, experimentation, and machine learning.
• Mentor and coach a growing team of Data Scientists, contributing to career development and technical excellence across the group.
• Lead analysis of interconnected marketplace systems and identify feedback loops across customer behavior, fulfillment reliability, ETA accuracy, pricing, supply planning, product experience, and business performance.
• Build causal inference and experimentation frameworks to determine which product, operational, and marketplace changes drive real business impact.
• Partner with engineering on architecture decisions for shared data layers, feature pipelines, modeling APIs, experimentation infrastructure, and production ML services.
• Design and implement experimentation strategies for changes that affect business metrics in high-noise environments.
• Drive business-impact-focused data science that combines causal inference, experimentation, risk-aware modeling, and scalable production ML systems that learn and adapt.

Requirements

• 8+ years of industry experience, or 6+ years with a PhD (MS acceptable where applicable) in Statistics, Economics, Applied Mathematics, Computer Science, Data Science, Machine Learning, or a related quantitative field.
• Proven experience applying data science and machine learning to complex business problems, including marketplace optimization, customer experience, forecasting, personalization, pricing, supply/demand balancing, operational policy changes, or product experimentation.
• 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.
• Strong judgment on business and product trade-offs (for example: customer experience vs. efficiency, ETA confidence vs. conversion risk, fulfillment reliability vs. cost, marketplace growth vs. quality, short-term optimization vs. long-term health).
• Proficiency in Python, including data analysis, visualization, and writing scalable, production-ready code with object-oriented design.
• Experience taking data science, ML, or causal inference systems into production, partnering with engineering on architecture, deployment, and monitoring best practices.
• Fluency in SQL or similar tools for interrogating production-scale datasets.
• Demonstrated ability to mentor and provide technical direction to other scientists, analysts, or engineers.

Technologies

• Python
• SQL
• A/B testing
• Difference-in-differences
• Regression discontinuity
• Instrumental variables
• Synthetic controls
• Uplift modeling
• Causal impact analysis

Benefits

• Competitive compensation package with equity
• 401(k)
• Choice of medical, dental, and vision plans
• Company paid short and long term disability coverage
• Paid time off, including flexible time off for exempt employees
• Paid vacation for non-exempt employees
• Paid sick leave in compliance with applicable law
• Paid parental leave
• Discounted meals and exclusive perks across the Wonder family of brands

Additional “Nice to Have”

• 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 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 with applied experimentation frameworks, including A/B testing, power analysis, heterogeneous treatment effects, guardrail metrics, interference effects, and long-term impact measurement.
• Experience building or influencing production ML systems that combine predictive modeling, causal measurement, experimentation, and business rules.
• Ability 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.

Hybrid Work Schedule

• Hybrid model requires 3 days per week in the office.
• Many team members choose to come in more often for in-person collaboration and connection.
• Eligible employees are welcome and encouraged to be in the office up to 5 days per week if it works for them.

Compensation: USD 216,000 - 249,500 per year.