Junior Data Science Engineer, DataRobot

• We are looking for a highly coachable, ambitious data science engineer who thrives in hands-on problem-solving and is eager to grow into a well-rounded AI/ML engineer
• This role is ideal for a highly motivated, coachable data scientist eager to work hands-on with Python, pandas, and modern AI tooling while making a real impact on customer success and AI adoption
• If you thrive in a fast-paced, highly autonomous environment, enjoy solving complex problems, and want to build AI solutions that truly scale, we’d love to hear from you!
• Strong Python ecosystem knowledge with the ability to experiment, iterate quickly, and troubleshoot real-world ML workflows using libraries like pandas, NumPy, scikit-learn, and web server tools like FastAPI
• 1-5 years of experience in data science, machine learning, or AI development (or a top-tier CS/DS graduate with strong fundamentals)
• Highly coachable, adaptable, and eager to learn—we prioritize raw ability, curiosity, and work ethic over specific years of experience
• Experience with ML model development, deployment, and evaluation—you should be comfortable turning data into insights and working with predictive models
• Some familiarity with data engineering best practices, including working with structured/unstructured data, feature engineering, and optimizing ML pipelines
• Proficiency in writing efficient, maintainable, and well-structured code, with an emphasis on reusability, scalability, and production readiness
• Experience with software engineering best practices, including containerization (Docker), CI/CD automation, and cloud-based ML deployment
• Ability to implement logging, monitoring, and debugging strategies to ensure the reliability and performance of AI/ML applications in production
• Strong foundation in Computer Science fundamentals, including object-oriented design, data structures, and algorithmic problem-solving
• Experience with APIs, SDK development, or ML platform integrations is a plus
• Customer-facing or project-based experience is a plus, but we’ll help develop these skills
• Self-motivated, driven, and eager to take on challenges—we’re looking for someone who thrives in a fast-moving, high-growth, and high-autonomy environment

Desirable

• 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