Translational Scientist, Applied Machine Learning and Agentic AI, Pharma R&D

Description

Data Expertise: Tempus has one of the largest multimodal patient datasets ever collected, providing a unique opportunity to work with extensive and diverse data. Become an expert in Tempus’ vast epidemiological, clinical, genomic, transcriptomic and pathology imaging data, along with the latest tools and techniques for their analysis and modeling.

Drug R&D Expertise: Work with leading pharmaceutical companies. Gain proficiency in their strategies, drug modalities, and pipelines to identify where the Tempus platform can add value.

Scientific Communication: Skillfully navigate client interactions to extract and communicate the most impactful insights driving new R&D opportunities; effectively communicate complex technical results and methodologies to diverse external stakeholders.

Personal development: Continuously immerse yourself in the latest industry trends, best practices, and advancements in machine learning and AI to revolutionize drug R&D

Qualifications

• Education and experience: Minimum: PhD (or Masters degree with 3+ years of relevant experience).
• Combining 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).
• Technical/Scientific Skills: Agentic Frameworks: Proficiency in Python and orchestration frameworks, specifically LangGraph (strongly preferred) or similar.
• Experience building deep agents with complex state management and graphs.
• LLM Application: Deep knowledge of prompt engineering, RAG (Retrieval-Augmented Generation), function calling, and evaluating non-deterministic LLM outputs.
• Machine Learning: Strong foundation in survival analysis (CoxPH, RSF) and evaluation metrics for oncology models.
• Software Engineering: 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.
• Communication Skills: Excellent written and verbal communication skills, with the ability to present complex information clearly and persuasively to diverse audiences.
• Motivated: Thrive in a fast-paced environment and willing to shift priorities seamlessly.

Preferred Skillsets/Background

• Experience in integrative modeling of multi-modal clinical and omics data, preferably with multimodal embeddings and foundation models.
• Strong understanding of data and artificial intelligence in Oncology.
• Understanding of cancer biology and clinical data.
• Experience with deploying ML models in cloud environments.