Data Scientist, Customer Engineering

Job Description:

DataRobot delivers AI that maximizes impact and minimizes business risk. Our platform and applications integrate into core business processes so teams can develop, deliver, and govern AI at scale. DataRobot empowers practitioners to deliver predictive and generative AI, and enables leaders to secure their AI assets. Organizations worldwide rely on DataRobot for AI that makes sense for their business — today and in the future. 

Job Description
The Data Scientist role in the Customer Engineering team plays a pivotal role at the intersection of AI/ML engineering, solution development, and go-to-market strategy. We are looking for an experienced data scientist who excels in hands-on problem-solving, can lead technical initiatives, and can serve as a trusted advisor to both internal teams and customers.
Your work will focus on designing, developing, and delivering production-ready AI solutions that accelerate customer adoption of the DataRobot platform, with a particular focus on SAP workflows.
Beyond solution development, you will work directly with clients in various industries as the DataRobot product and data science subject matter expert.
This role is ideal for a motivated data scientist with proven experience who wants to work hands-on with Python, pandas, and modern AI tooling while making a significant impact on customer success and AI adoption. If you thrive in a fast-paced, highly autonomous environment and want to build AI solutions that truly scale, we'd love to hear from you!Key Responsibilities:

• Design and develop sophisticated, production-ready assets that accelerate AI/ML adoption for customers, ranging from reusable solution templates to deployable frameworks.
• Lead the implementation of AI/ML workflows using Python, pandas, and modern AI tooling, ensuring they are scalable, maintainable, and customer-ready.
• Establish and champion 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).
• Create comprehensive testing strategies and implement automated tests for AI/ML workflows.
• Lead cross-functional collaboration with product, sales, and marketing teams to scale high-impact solutions.
• Address complex real-world deployment challenges, including monitoring, logging, and improving reliability in AI/ML workflows.
• Serve as a technical liaison in customer engagements, representing the DataRobot product to various personas from data scientists to C-level executives.
• Lead proof of value processes and quantify the business impact of DataRobot solutions.
• Ensure the success of our customers by collaborating with business stakeholders to ensure that AI solutions deliver successful business outcomes
• Stay ahead of industry trends, continuously refining our approaches and advocating for best practices in AI/ML engineering.
• Work closely with enablement teams to develop documentation, content, and training materials that scale adoption of our solutions.

What this Role is Not:

• A SWAT demo team - while you will be customer-facing, this role is about building reusable solutions, not one-off demos.
• A technical marketing role - you'll collaborate with marketing but won't be driving content strategy.
• A pure research role - we need hands-on builders who can ship working solutions that make an impact.

Knowledge, Skills and Abilities:

• Strong Python ecosystem expertise with the ability to design, develop, and troubleshoot complex ML workflows using libraries like pandas, NumPy, scikit-learn, and web server tools like FastAPI.
• 3-5 years of experience in data science, machine learning, or AI development with a proven track record of delivering production solutions.
• Experience with ML model development, deployment, and evaluation. You should be comfortable leading data-to-insights projects and optimizing predictive models.
• Strong data engineering capabilities, 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.
• Experience with APIs, SDK development, or ML platform integrations.
• Proven experience in consultative sales processes in the data/analytics marketplace, with the ability to translate complex technical concepts into business value.
• Excellent communica