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

About the Role

Passionate about precision medicine and advancing the healthcare industry? Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time. 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.

Responsibilities

• 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, and 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 Master’s 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, and a 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.

Pay

USA: $140,000-$200,000
NYC/SF: $150,000-$210,000
The expected salary range may vary for other locations. Actual salary may vary based on qualifications and experience. Tempus offers a full range of benefits, which may include incentive compensation, restricted stock units, medical and other benefits depending on the position.