Grubhub is looking for a Data Science Solutions Engineer to build robust experimentation, machine learning, and decision modeling solutions for teams across the company. These solutions will drive key products like delivery estimation and dispatch decisions, and provide a platform for managing and analyzing experiments.
Requirements
- 3+ years in software engineering with a focus on MLOps, Python, and cloud-based environments (AWS preferred).
- Proven experience in building and maintaining CI/CD pipelines, managing monorepos, and scaling machine learning models in production.
- Proficiency in Python, containerization, orchestration tools, and experience with data versioning and model management tools.
- Additional experience in Java is a plus.
- Front-end experience with Flask or React is a plus.
- Experience or knowledge of training, deploying and monitoring ML models is a plus.
- Experience with distributed systems and microservices architecture is a plus.
Responsibilities
- Assist in the design and implementation of experimentation, MLOps and decision modeling pipelines that enable our data scientists to iterate on and deploy changes efficiently.
- Help implement monitoring frameworks to maintain system observability and quickly address any issues, contributing to minimizing SEV incidents.
- Contribute to platform improvements by exploring tools and integrations that enhance the data science workflow and ensure smooth integration with the GrubHub platform.
- Collaborate with data scientists to implement best practices in coding, testing, and version control, contributing to the overall quality and reliability of our codebase.
- Assist in establishing processes for better data lineage, documentation, and ownership across datasets, reducing inconsistencies and promoting team autonomy.
- Participate in the development of systems for data versioning, model management, and deployment strategies, ensuring models are manageable and easy to deploy.
Other
- Strong communication skills, with the ability to work closely with data scientists, product managers, and other engineers.
- An emphasis on knowledge sharing and teamwork.
- Passion for staying up-to-date with the latest trends in MLOps, machine learning, and software engineering, with a drive to continuously improve and innovate.
