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Grubhub
Grubhub
1,001 – 5,000 Employees
H1B Visa SponsorFood & BeverageLogisticsMarketplace
Grubhub is a U.S. food ordering and delivery marketplace that brings diners, restaurants, and delivery drivers together. As part of Wonder, it runs customer ordering platforms and the Grubhub+ subscription service, using technology and logistics to support restaurant partnerships and coordinate deliveries. Its work spans food and beverage, logistics, and marketplace operations, offering job seekers context on the business behind its roles.
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Senior Staff Data Scientist, Marketplace

Set data science and machine learning direction for Wonder’s marketplace, guiding causal inference, experimentation, production systems, and cross-functional technical work.

USUnited StatesILIllinoisNYNew York
$216,000 – $249,500 / year
HybridFull-timeSeniorEnglish RequiredData Scientist
PythonSQL

Description

  • Set principles and practices for applying data, experimentation, and machine learning to customer, marketplace, and business outcomes.
  • Coach Data Scientists and support their career growth and technical development.
  • Investigate interconnected marketplace systems and their feedback loops.
  • Build causal inference and experimentation frameworks to measure business impact.
  • Collaborate with engineering on shared data layers, feature pipelines, modeling APIs, experimentation infrastructure, and production ML services.
  • Design and deliver experiments to assess changes to business metrics in high-noise settings.
  • Advance data science that combines causal inference, experimentation, risk-aware modeling, and scalable production ML to drive business results.
  • Find high-impact opportunities in marketplace efficiency, customer experience, ETA accuracy, fulfillment reliability, pricing, supply planning, demand forecasting, and operations.
  • Prototype and test approaches, shape architecture, and put models and insights into practice to improve business metrics.

Requirements

  • Bring at least 8 years of industry experience with a master’s degree, or 6 years with a PhD, in Statistics, Economics, Applied Mathematics, Computer Science, Data Science, Machine Learning, or a related quantitative field.
  • Demonstrated success applying data science and machine learning to complex business problems.
  • Deep knowledge of 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.
  • Proficiency in Python, data analysis, visualization, and writing scalable, production-ready code using object-oriented design.
  • Experience taking data science, machine learning, or causal inference systems into production.
  • Fluency in SQL or similar tools for querying production-scale datasets directly.
  • Experience mentoring and providing technical direction to scientists, analysts, or engineers.
  • End-to-end leadership in designing data science, machine learning, measurement, or experimentation frameworks is a plus.
  • Experience designing causal measurement strategies for complex systems is a plus.
  • Background in causal inference, econometrics, Bayesian modeling, experimental design, or observational measurement is a plus.
  • Experience with applied experimentation frameworks is a plus.
  • Experience building or influencing production machine learning systems is a plus.
  • Cross-disciplinary influence is a plus.
  • Experience setting strategy and technical roadmaps is a plus.
  • Must meet employment eligibility verification requirements through E-Verify.

Benefits

  • Equity.
  • 401(k) plan.
  • 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 accordance with applicable law.
  • Paid parental leave.
  • Discounted meals.
  • Exclusive perks across the Wonder family of brands.

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