Sr. Data Scientist, Translational Research

Tempus AI · Vertical AI · Chicago, IL +1

This role focuses on building specialized AI agents and workflows that leverage LLMs to help clients derive insights from large-scale biomedical data. It involves client onboarding, scientific problem-solving, platform guidance, and creating AI-assisted tools for data analysis. The role requires experience in data science, machine learning, Python/R, and putting data science workflows into production, with a strong emphasis on using AI-assisted development tools.

What you'd actually do

  1. 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.
  2. 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).
  3. Lead the technical and scientific onboarding process for new clients, ensuring a smooth transition into the Tempus Data Ecosystem and our analytical platform, Lens.
  4. Act as the voice of the client, collaborating closely with Tempus Product, Engineering, and Data Science teams to prioritize features and resolve technical challenges.
  5. Create high-quality technical documentation, tutorials, and training materials for clients on platform features and best practices for scientific analysis.

Skills

Required

  • 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 with statistical modeling, data mining and/or machine learning methods
  • Fluent in Python or R
  • Experience with software development and the AWS or GCP technical stack
  • Experience with engineering practices for research computing (Docker, Git, Github, Linux, cloud computing)
  • Demonstrated proficiency 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
  • A strong track record of identifying inefficiencies or scientific roadblocks and developing pragmatic, user-friendly solutions

Nice to have

  • R coding experience (if Python is primary)
  • Experience in the biotech, pharma, or healthcare technology space
  • Direct experience with oncology research
  • Pandas
  • NumPy
  • SciPy
  • Scikit-learn
  • Jupyter Notebooks
  • RStudio
  • R Package development
  • tidyverse
  • ggplot
  • Git
  • matplotlib
  • seaborn
  • HTML5
  • CSS3
  • JavaScript
  • D3
  • Plot.ly
  • Flask
  • Dask

What the JD emphasized

  • Experience building specialized AI agents or designing workflows that utilize LLMs to solve complex technical problems
  • Demonstrated proficiency with AI-assisted development tools (e.g., GitHub Copilot in VS Code) to optimize coding efficiency and troubleshooting
  • Experience putting data science workflows into production

Other signals

  • 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
  • Experience building specialized AI agents or designing workflows that utilize LLMs to solve complex technical problems
  • Experience putting data science workflows into production