Staff to Senior Data Scientist - Causal ML & Experimentation

Grubhub Holdings Inc.; New York (NY); On-site; USD 180,000 - 240,000; Full time

Job summary

Wonder Data Science is hiring staff to senior staff data scientists to build data science and ML systems that improve marketplace operations, customer experience, and business decision-making.

You will identify opportunities across marketplace efficiency, ETA accuracy, pricing, supply planning, and performance. You will design rigorous causal and experimental frameworks, mentor a growing team, and partner with engineering to productionize models and insights that measurably move business metrics.

Qualifications

• 6-8+ 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.

Responsibilities

• 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.
• Design statistically rigorous frameworks to understand causal impact, separate signal from noise, and guide business strategy through experimentation, measurement, and principled inference.
• 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.
• Prototype, experiment, influence architecture, and ensure we operationalize models and insights that actually move business metrics - not just analyses that look good offline.
• 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.

Skills

Causal inference

Experimentation

Python

SQL

Data analysis

Production-ready code

Mentoring

Education

MS in Statistics, Economics, Applied Mathematics, Computer Science, Data Science, Machine Learning

PhD in Statistics, Economics, Applied Mathematics, Computer Science, Data Science, Machine Learning

Tools

Pandas

Scikit-learn

TensorFlow/PyTorch

Job description

Wonder Data Science is hiring staff to senior staff data scientists to build data science and ML systems that improve marketplace operations, customer experience, and business decision-making.

You will identify opportunities across marketplace efficiency, ETA accuracy, pricing, supply planning, and performance. You will design rigorous causal and experimental frameworks, mentor a growing team, and partner with engineering to productionize models and insights that measurably move business metrics.