Sr. Data Scientist, Translational Research

Key Responsibilities

• Lead the technical and scientific onboarding process for new clients, ensuring a smooth transition into the Tempus Data Ecosystem and our analytical platform, Lens.
• Engage with clients (primarily researchers and scientists) to deeply understand their specific scientific hypotheses and questions (e.g., biomarker discovery, target identification, clinical trial design).
• Translate the client's scientific needs into actionable steps on the Tempus analytical platform. This includes designing workflows, structuring queries, and guiding the analysis of complex datasets (genomic, clinical, imaging).
• Leverage LLMs and co-pilot tools to accelerate internal development and create specialized agents that help clients navigate and derive insights from large-scale biomedical data, and make the client experience on the platform as easy, efficient, and intuitive as possible.
• Act as the voice of the client, collaborating closely with Tempus Product, Engineering, and Data Science teams to prioritize features and resolve technical challenges.
• Create high-quality technical documentation, tutorials, and training materials for clients on platform features and best practices for scientific analysis.

Qualifications

• A Master’s or Ph.D. in a relevant scientific field (e.g., Computational Biology, Bioinformatics, Genomics, Data Science, or a related life science discipline).
• Demonstrated experience working with and analyzing large-scale biomedical datasets (e.g., Next-Generation Sequencing data, clinical trial data, real-world data).
• Experience working with statistical modeling, data mining and/or machine learning methods.
• Hands-on experience with analytical tools and languages relevant to biomedical research. Must be fluent in Python or R. If Python is the primary language, having some R coding experience is required. Being comfortable in both languages is an added bonus.
• Experience with software development and the AWS or GCP technical stack.
• Experience with engineering practices for research computing (Docker, Git, Github, Linux, cloud computing).
• Experience with AI-assisted development tools (e.g., GitHub Copilot in VS Code) to optimize coding efficiency and troubleshooting.
• Experience building specialized AI agents or designing workflows that utilize LLMs to solve complex technical problems.
• Experience putting data science workflows into production.
• Proven ability to work collaboratively in a team environment and thrive in a fast-paced environment, willing to shift priorities seamlessly.
• Excellent written and verbal communication skills with a proven ability to explain complex technical and scientific concepts to both technical and non-technical audiences.
• Proven ability to identify inefficiencies or scientific roadblocks and develop pragmatic, user-friendly solutions.