Grubhub is looking to improve its fulfillment services by building predictive ML models for delivery estimation, dispatch, scheduling, and driver pay to enhance efficiency, diner satisfaction, and operational costs.
Requirements
- 3+ years of experience with machine learning libraries such as XGBoost, PyTorch, TensorFlow, or similar frameworks.
- Familiarity with decision trees, gradient boosting, and deep learning techniques.
- Strong proficiency in Python, including data manipulation libraries (Pandas, NumPy) and distributed computing frameworks (PySpark, Hive).
- Hands-on experience with data preparation, feature engineering, and handling large datasets in a distributed environment.
- Experience in logistics, fulfillment, or operations is a plus.
Responsibilities
- Design, build, and optimize machine learning models to improve delivery estimation, dispatching efficiency, driver pay optimization, and scheduling reliability.
- Leverage tools like H2O, XGBoost, and AWS (Sagemaker, EMR, S3, EC2) to implement scalable solutions that meet Grubhub's high performance and accuracy standards.
- Establish and monitor KPIs for ML models, continuously improving business outcomes such as fulfillment efficiency, diner satisfaction, and operational costs.
- Participate in discussions for improving the MLOps framework for streamlined model development and deployment, ensuring robust performance and reproducibility.
- Communicate findings, model performance, and recommendations effectively to technical and non-technical stakeholders.
Other
- Advanced degree in a quantitative field such as Computer Science, Data Science, Mathematics, Engineering, Statistics, or equivalent experience.
- Proven ability to translate business requirements into ML solutions with measurable impact.
- Ability to clearly articulate complex technical ideas to diverse audiences, including leadership and non-technical stakeholders.
- A passion for tackling ambiguous, complex problems and delivering innovative solutions.
- A growth mindset with a deep interest in staying updated on the latest advancements in machine learning and logistics.
