Senior Manager, AI and Data Science

About this role

Madrigal is a biopharmaceutical company focused on delivering novel therapeutics for metabolic dysfunction-associated steatohepatitis (MASH), a serious liver disease that can progress to cirrhosis, liver failure, need for liver transplantation and premature mortality. Every member of our Madrigal team is connected by our shared purpose: leading the fight against MASH.   Madrigal’s medication, Rezdiffra (resmetirom), is a once-daily, oral, liver-directed THR-β agonist designed to target key underlying causes of MASH. Rezdiffra is the first and only medication approved by both the FDA and European Commission for the treatment of MASH with moderate to advanced fibrosis (F2 to F3). An ongoing Phase 3 outcomes trial is evaluating Rezdiffra for the treatment of compensated MASH cirrhosis (F4c).   Our success is driven by our people. We are building a dynamic, inclusive, and high-performing culture that values scientific excellence, operational rigor, and collaboration. To support our continued growth, we are strengthening our workforce strategy to ensure we have the right talent, at the right time, in the right way.

Madrigal Pharmaceuticals is advancing transformational therapies with scientific rigor and   a commitment   to patients. Within Medical Affairs, we turn scientific evidence —   publications , real-world evidence (RWE), HEOR, and field-medical insight — into decisions that advance patient care, and we   operate   a growing AI platform with multiple production agents supporting that work. We are seeking a Senior Manager, AI and Data Science who brings strong technical depth, hands-on experience with agentic AI, and an ownership mindset. This individual will own modular, high-impact workstreams end-to-end, from design through production — combining agentic AI and large language model (LLM) engineering with applied machine learning on real-world evidence — working alongside the platform’s architect and Medical Affairs stakeholders.  

We welcome candidates from a range of   scientific, medical,   and engineering backgrounds. What unites strong applicants is rigor and an evidence-driven approach to building and evaluating AI systems, an ownership mindset, and the ability to collaborate across scientific, technical, and business teams. The ideal candidate is comfortable owning ambiguous problems end-to-end.  

Key Responsibilities  

Agentic AI Systems  

• Build and   operate   production AI agents and orchestration workflows using modern agentic frameworks such as   LangGraph   and   deepagents .  

• Design modular, reusable components for reasoning, retrieval, tool integration (including MCP), and workflow automation.  

• Partner with the Agentic UI team to deliver seamless, end-to-end products.  

Retrieval, RAG & Evaluation  

• Own and   optimize   the retrieval stack, including vector and hybrid search,   embeddings , chunking, and reranking.  

• Build evaluation and regression safeguards for LLM outputs, with particular attention to   citation   faithfulness and   groundedness , which are essential in Medical Affairs.  

Applied Machine Learning on Real-World Evidence  

• Design and deploy machine learning on real-world evidence (claims, EHR, registry), HEOR, and field-medical data to surface Medical Affairs insights.  

• Apply embeddings,   predictive models , and statistical methods where they are the right fit for the problem.  

Data Integration &   MLOps  

• Automate and scale data-ingestion pipelines into   AI/ ML-ready data on platforms such as Databricks  

• Own deployment, monitoring, and observability (for example, using   LangSmith ) for the systems you ship.  

Cross-Functional Engagement & Governance  

• Translate complex AI concepts into clear, actionable insights for technical and non-technical Medical Affairs partners.  

• Align AI initiatives with enterprise governance, privacy, and PHI and compliance expectations.  

Qualifications  

Required  

• A demonstrated record of scientific rigor: either a Ph.D. in a quantitative or medical-adjacent field with first-author publications, or a strong engineering background paired with peer-reviewed research at top venues (for example,   NeurIPS , ICML, ICLR, or ML4H). The specific credential matters less than the demonstrated rigor and record of shipping.  

• 7+   years of applied machine learning or data science, including 3+ years shipping ML or AI systems to production .  

• 1+   year   of hands-on agentic AI development using modern frameworks (for example,   LangGraph ,   deepagents , or   LangSmith ).  

• Strong RAG and retrieval engineering, including vector and hybrid search, embeddings, and reranking, with the ability to evaluate   groundedness   and citation accuracy.  

• Applied ML on real-world or patient-level data (claims, EHR, registry), including feature engineering.  

• Experience with production reliability, data access and authentication, and handling sensitive (PHI) data.  

• Strong communication   skills and the ability to collaborate across scientific, technical, and business teams.  

• Experience in Healthcare or Life Sciences.  

Preferred  

• Production experience with   deepagents ,   LangGraph , and   LangSmith ; MCP tools; Azure; and Databricks Unity Catalog, Microsoft Fabric, or   OneLake .  

• Proven   track record   of deploying agents to production at scale, including LLM evaluation and observability.  

• Background in real-world evidence, epidemiology, or HEOR analytics.  

• Medical Affairs experience.  

Madrigal’s Total Rewards strategy is based on a biotech industry peer group comparator and is inclusive of base pay, bonus and equity. Our equity offers meaningful opportunity allowing our employees to share in the success they help create. By aligning individual and company performance, we empower employees to think like owners, giving them a stake in the organization.

All employees receive equity, which we believe reinforces our ownership culture. B

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