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

Tempus AI•Boston, MA

37d•$140,000 - $210,000

About The Position

The Senior Scientist, Applied Machine Learning and Generative AI, Pharma R&D will perform complex computational analyses and develop algorithms, causal models, and agent-based tools to advance the Tempus platform supporting drug R&D. The ideal candidate will possess strong applied machine learning, causal inference, and generative AI skills, with experience building and applying LLMs, agentic systems, causal frameworks, and foundation models in the life sciences. The candidate will also be proficient in communicating complex findings to various stakeholders. 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. 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 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

Requirements

PhD (or Masters degree with 2+ years of relevant experience) plus an additional 2+ years of relevant industry or post-doctoral experience.
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).
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.
Proven track record of success in peer-reviewed publications.
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.
Thrive in a fast-paced environment and willing to shift priorities seamlessly.

Nice To Haves

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.

Responsibilities

Perform complex computational analyses and develop algorithms, causal models, and agent-based tools to advance the Tempus platform supporting drug R&D.
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.
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.
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.
Work with leading pharmaceutical companies, gaining proficiency in their strategies, drug modalities, and pipelines to identify where the Tempus platform can add value.
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.
Empower computational biologists and RWE scientists through targeted AI guidance and hands on coaching to increase AI tool adoption to maximize impact.
Continuously immerse yourself in the latest industry trends, best practices, and advancements in machine learning and AI to revolutionize drug R&D.