Senior Staff Data Scientist

Wonder Group•Chicago, NY

15h•Hybrid

About The Position

At Wonder Data Science, our mission is to build data science and machine learning systems that improve how our marketplace operates, how customers experience the platform, and how the business makes high-quality decisions. As a Senior Staff Data Scientist, you will go beyond individual problem solving — you will help shape the strategic direction of applied data science, mentor senior and junior scientists, and collaborate closely with engineering, product, operations, and business leaders to move our ML and analytics capabilities toward scalable, production-grade systems. You will identify high-leverage opportunities across the business, including marketplace efficiency, customer experience, ETA accuracy, fulfillment reliability, pricing strategy, supply planning, demand forecasting, and operational performance. You will design statistically rigorous frameworks to understand causal impact, separate signal from noise, and guide business strategy through experimentation, measurement, and principled inference. You will help define how we structure trade-offs like customer experience vs. operational efficiency, speed vs. cost, prediction accuracy vs. business impact, short-term metric movement vs. long-term marketplace health, and automation vs. human judgment. You’ll prototype, experiment, influence architecture, and ensure we operationalize models and insights that actually move business metrics — not just analyses that look good offline.

Requirements

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.
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 — 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.
Demonstrated ability to take 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 directly interrogating production-scale datasets.
Experience mentoring and providing technical direction to other scientists, analysts, or engineers.

Nice To Haves

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
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.

Responsibilities

Serve as a technical thought leader in Data Science — defining principles, frameworks, and best practices for how Wonder uses data, experimentation, and machine learning to improve customer, marketplace, and business outcomes.
Mentor and coach a growing team of Data Scientists and contribute to career development and technical excellence across the group.
Lead the exploration of interconnected marketplace systems, recognizing feedback loops between customer behavior, fulfillment reliability, ETA accuracy, pricing, supply planning, product experience, and business performance.
Develop causal inference and experimentation frameworks that help Wonder understand which product, operational, and marketplace changes truly drive business impact.
Partner with engineering to drive architecture decisions for shared data layers, feature pipelines, modeling APIs, experimentation infrastructure, and production ML services.
Define and implement robust experimentation strategies for changes that move business metrics in high-noise environments.
Champion business-impact-driven data science, integrating causal inference, experimentation, risk-aware modeling, and scalable production ML systems that learn and adapt.