Translational Scientist, Applied Machine Learning, Agentic AI
Job in
Aurora, Kane County, Illinois, 60505, USA
Listed on 2026-07-20
Listing for:
Jobtailor
Full Time
position Listed on 2026-07-20
Job specializations:
-
IT/Tech
AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Scientist
Job Description & How to Apply Below
- Contribute to the technical development of cutting-edge agentic frameworks designed to automate the discovery of novel prognostic and predictive models in oncology
- Responsible for building and refining "deep agents" capable of hypothesis generation, experimental design, and multimodal ML modeling utilizing foundation models
- Key technical contributor, working closely with senior scientists and engineers to implement system designs and ensure code quality
- Apply advanced scientific methodologies to develop new predictive models and utilize causal inference frameworks to analyze vast multimodal oncology data
- Collaborate with Research, Engineering & Data Science teams across Tempus’ expansive data science community to develop and deliver innovative computational solutions
- Work with leading pharmaceutical companies to identify where the Tempus platform can add value
- Skillfully navigate client interactions to extract and communicate impactful insights driving new R&D opportunities
- Minimum:
PhD (or Masters degree with 3+ years of relevant experience) - Quantitative and computational skills, specifically in AI agent based workflows (e.g. Applied Machine Learning, Generative AI, Mathematics, biostatistics)
- Biological, medical, or drug development knowledge and data (e.g. oncology, RWE, medical science, or clinical drug development)
- Proficiency in Python and orchestration frameworks, specifically Lang Graph (strongly preferred) or similar
- Experience building deep agents with complex state management and graphs
- Deep knowledge of prompt engineering, RAG (Retrieval-Augmented Generation), function calling, and evaluating non-deterministic LLM outputs
- Strong foundation in survival analysis (CoxPH, RSF) and evaluation metrics for oncology models
- Adherence to software best practices (unit testing, git) and experience designing scalable systems
- Experience working with clinical trial or real-world data, clinical guidelines (e.g., NCCN for oncology) and emerging RWE methodologies
- Track record of success: proven in peer reviewed publications or other proven impact.
- Excellent written and verbal communication skills, with the ability to present complex information clearly and persuasively to diverse audiences.
- Thrive in a fast-paced environment and willing to shift priorities seamlessly.
- Python
- AI agent based workflows
- Applied Machine Learning
- Generative AI
- Mathematics
- biostatistics
- deep agents
- prompt engineering
- survival analysis
- evaluation metrics
- communication skills
- collaboration
- client interaction
- adaptability
- problem-solving
- PhD
- Masters degree
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