Senior Staff Data Scientist

Wonder • Chicago, IL

1d • $240,000 - $249,500 • Hybrid

About The Position

At Wonder Data Science, the Senior Staff Data Scientist helps shape the strategic direction of applied data science and machine learning, mentors scientists, and partners with engineering, product, operations, and business leaders to build scalable, production-grade systems. The role identifies high-leverage opportunities across marketplace efficiency, customer experience, ETA accuracy, fulfillment reliability, pricing, supply planning, demand forecasting, and operational performance. It uses rigorous statistical frameworks, experimentation, measurement, and causal inference to guide business strategy and distinguish meaningful impact from noise.

The role defines approaches to business trade-offs, prototypes and experiments, influences technical architecture, and operationalizes models and insights that improve business metrics. It provides technical leadership, mentors and coaches the data science team, studies interactions across marketplace systems, develops causal inference and experimentation frameworks, and partners with engineering on shared data layers, feature pipelines, modeling APIs, experimentation infrastructure, and production ML services. The position champions business-impact-driven data science through causal inference, experimentation, risk-aware modeling, and scalable production ML systems.

The role follows a hybrid model requiring three days per week in the office, with the option and encouragement to work onsite up to five days per week. Listed annual salary ranges are $240,000–$249,500 in New York and $216,000–$224,500 in Illinois; offers vary based on location and candidate qualifications and experience.

Requirements

• At least 8 years of industry experience with an MS, or at least 6 years with a PhD, 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, such as 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 intuition for business and product trade-offs, including customer experience versus efficiency, ETA confidence versus conversion risk, fulfillment reliability versus cost, marketplace growth versus quality, and short-term optimization versus long-term health.
• Proficiency in Python, data analysis, visualization, and writing scalable, production-ready code using object-oriented design.
• Demonstrated ability to take data science, machine learning, or causal inference systems into production and partner with engineering on architecture, deployment, and monitoring best practices.
• Fluency in SQL or similar tools for interrogating production-scale datasets.
• Experience mentoring and providing technical direction to scientists, analysts, or engineers.

Nice To Haves

• Experience leading end-to-end design of data science, machine learning, measurement, or experimentation frameworks in 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 combining predictive modeling, causal measurement, experimentation, and business rules.
• Ability to influence across disciplines and 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.

Responsibilities

• Serve as a technical thought leader, defining principles, frameworks, and best practices for data, experimentation, and machine learning.
• Mentor and coach Data Scientists and contribute to their career development and the group's technical excellence.
• Explore interconnected marketplace systems and identify feedback loops across customer behavior, fulfillment reliability, ETA accuracy, pricing, supply planning, product experience, and business performance.
• Develop causal inference and experimentation frameworks to determine the impact of product, operational, and marketplace changes.
• Partner with engineering on architecture decisions for shared data layers, feature pipelines, modeling APIs, experimentation infrastructure, and production ML services.
• Define and implement experimentation strategies for changes affecting business metrics in high-noise environments.
• Champion business-impact-driven data science by integrating causal inference, experimentation, risk-aware modeling, and scalable production ML systems.
• Identify high-leverage business opportunities and operationalize models and insights that improve business metrics.

Benefits

• Competitive compensation package with equity and a 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, and paid sick leave in compliance with applicable law.
• Paid parental leave.
• Discounted meals and exclusive perks across the Wonder family of brands.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior