Junior Data Science Engineer
@ DataRobot
Summary
About the Company
Company Name: DataRobot
Industry: Artificial Intelligence
Size: 1001+ employees
Overview: Value-Driven AI for business
Benefits
Medical, Dental & Vision Insurance, Flexible Time Off Program, Paid Holidays, Paid Parental Leave, Global Employee Assistance Program (EAP), Work from Home Opportunities
Perks: A World-class Team, Company Outings
Job Description
The Junior Data Science Engineer role in the Customer Engineering team is an exciting opportunity to work at the intersection of AI/ML engineering, solution development, and go-to-market strategy.
Your work will focus on building reusable, production-ready AI solutions that accelerate customer adoption of the DataRobot platform. This means:
- Developing scalable, productionizable AI assets that customers can confidently integrate into their workflows
- Ensuring AI solutions are resilient, maintainable, and easy to deploy, minimizing operational friction
- Engaging with early-adopter customers and internal teams to refine and validate solutions
- Collaborating with product, sales, marketing, and enablement teams to drive solution awareness and usage at scale
Beyond developing and refining AI solutions, you will also help prioritize future investments by identifying high-impact opportunities and gathering customer feedback to refine existing assets.
Develop reusable, production-ready assets that accelerate AI/ML adoption for customers—ranging from demo environments to deployable templates.
Prototype and experiment with AI/ML workflows using Python, pandas, and modern AI tooling, ensuring they are scalable and customer-ready.
Implement and refine engineering best practices to improve performance, scalability, and maintainability of AI/ML solutions.
Work within existing infrastructure to support scalable AI deployments, including CI/CD automation, API integrations, and containerized environments (Docker, Kubernetes).
Contribute to, create, and maintain automated tests for AI/ML workflows.
Collaborate cross-functionally with product, sales, and marketing teams to scale high-impact solutions.
Work on real-world deployment challenges, including monitoring, logging, and improving reliability in AI/ML workflows.
Support customer engagements by working directly with users to validate solutions, improve adoption, and ensure real-world impact.
Stay ahead of industry trends, continuously refining our approaches and advocating for best practices in AI/ML engineering.
Work closely with enablement teams to scale adoption of our solutions through documentation, content, and training materials.