Senior Scientist I, Applied Machine Learning and Generative AI, Pharma R&D

Description

• Data Expertise: 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.
• Innovation: Drive continual improvement of the Tempus platform for pharmaceutical R&D by championing and building new machine learning and generative AI capabilities based on client needs and industry trends.
• Teamwork and collaboration: Work with Research, Engineering & Data Science teams across Tempus' expansive data science community to develop and deliver innovative computational solutions; co-develop solutions with Pharma partner science and clinical teams.
• 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.
• Scientific Leadership & Influence: Empower computational biologists and RWE scientists through targeted AI guidance and hands on coaching to increase AI tool adoption to maximize impact.
• 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 2+ years of relevant experience).
• Plus an additional 2+ years of relevant industry or post-doctoral experience.
• Combining: Quantitative and computational skills, with a focus on causal AI, causal inference, and/or explainable AI (e.g. Causal 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: Proficient in R, Python, and SQL, with specific expertise in frameworks for agentic orchestration (e.g., LangChain, LangGraph, AutoGen, or DSPy).
• Deep knowledge of machine learning and statistical modeling, with hands-on experience in causal methodologies (e.g., Directed Acyclic Graphs, counterfactual reasoning, and heterogeneous treatment effect estimation).
• Applicable knowledge of LLM-driven agent architectures, including experience with prompt engineering, RAG (Retrieval-Augmented Generation), and function calling/tool use.
• Awareness of the uses of machine learning in molecular/biomedical data analysis or drug discovery/development.
• Experience working with molecular data, clinical trial and/or real-world data.
• Track record of success: proven in peer reviewed publications.
• Communication Skills: Excellent written and verbal communication skills, with the ability to present complex information clearly and persuasively to diverse audiences. Comfort in a client-facing role and ability to deliver technical training to both internal and external 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.
• Strong understanding of data and artificial intelligence in drug R&D.
• Understanding of cancer biology.
• Previous experience working with large transcriptome and NGS data sets, or clinical or real-world medical data.