Senior Data Scientist - Lead Causal ML & Production Systems

Grubhub Holdings Inc.; Chicago (IL); Hybrid; USD 216,000 - 225,000; Full time

Benefits offered by this job

Equity

401(k)

Medical, dental & vision plans

Paid time off

Job summary

Wonder Data Science is seeking a Senior Staff Data Scientist to shape the strategic direction of applied data science, mentor junior scientists, and collaborate with engineering, product, and operations to productionize ML models and insights.

You will design causal inference frameworks, guide experimentation, and help optimize marketplace efficiency, ETA accuracy, pricing, and operation performance in a hybrid office setting (New York and Illinois).

Qualifications

• 8+ years of industry experience with MS or 6+ years with PhD in Statistics, Economics, Applied Mathematics, CS, Data Science, ML, or related field.
• Proven experience applying data science and ML to complex business problems such as marketplace optimization, forecasting, pricing, or experimentation.
• Deep expertise in causal inference, experimentation, and statistical modeling with several methods listed.

Responsibilities

• Serve as technical thought leader shaping data science principles and best practices.
• Mentor and coach a growing team of Data Scientists.
• Lead exploration of marketplace systems and causal frameworks.
• Partner with engineering on architecture, deployment, and monitoring of production ML systems.

Skills

Data Science

Machine Learning

Causal Inference

Experimentation

Python

SQL

Production ML

Mentoring

Education

MS or PhD in related field

Tools

Python

SQL

Airflow

Docker

Job description

Wonder Data Science is seeking a Senior Staff Data Scientist to shape the strategic direction of applied data science, mentor junior scientists, and collaborate with engineering, product, and operations to productionize ML models and insights.

You will design causal inference frameworks, guide experimentation, and help optimize marketplace efficiency, ETA accuracy, pricing, and operation performance in a hybrid office setting (New York and Illinois).