R&D AI Manager

ZS is a place where passion changes lives. As a management consulting and technology firm focused on improving life and how we live it, we transform ideas into impact by bringing together data, science, technology and human ingenuity to deliver better outcomes for all. Here you’ll work side-by-side with a powerful collective of thinkers and experts shaping life-changing solutions for patients, caregivers and consumers, worldwide. ZSers drive impact by bringing a client-first mentality to each and every engagement. We partner collaboratively with our clients to develop custom solutions and technology products that create value and deliver company results across critical areas of their business. Bring your curiosity for learning, bold ideas, courage and passion to drive life-changing impact to ZS. What you'll do: R&D AI Manager will… AI Strategy & Solution Design Design, build, and deploy AI/ML solutions across the R&D value chain, with a focus on Clinical Development and/or Medical Affairs (e.g., trial design, clinical operations, evidence generation, medical content, scientific engagement), ensuring regulatory‑ and compliance‑by‑design delivery. Translate complex R&D and Medical problems into scalable, decision‑centric AI solutions, integrating structured and unstructured data (clinical, operational, real‑world, literature, and medical content). Data Science & AI Leadership Define and execute data science strategies aligned to R&D and Medical priorities, balancing scientific rigor, business impact, and operational feasibility. Lead the development of diagnostic predictive, prescriptive, and generative models, applying appropriate techniques based on the problem definition and available data Ensure models are explainable, validated, and production‑ready within regulated life sciences environments. Client Engagement & Growth Lead and contribute to RFPs, proposals, POVs, and pilots, shaping compelling AI narratives grounded in client value and real‑world feasibility. Serve as a trusted advisor to client stakeholders in R&D, Medical, and Digital, helping them prioritize AI use cases, design roadmaps, and move from pilots to scaled impact. Partner closely with consulting, technology, and product teams to drive integrated, cross‑functional delivery. Program & Delivery Ownership Own end‑to‑end project execution, including scoping, delivery planning, risk management, and quality assurance, ensuring on‑time, high‑impact outcomes. Mentor and guide junior data scientists and analysts, setting high standards for analytical quality, storytelling, and client engagement. Capability Building & Innovation Play a key role in building repeatable data science assets, accelerators, and platforms that scale AI delivery across Clinical Development and Medical Affairs. Stay current on advances in AI/ML, life sciences R&D, and evolving regulatory expectations, proactively translating these into new offerings and differentiated client value. Contribute to internal and external thought leadership, including whitepapers, conference presentations, and publications in areas such as Clinical AI, Medical AI, and evidence generation. Communication & Influence Communicate complex analytical findings clearly to technical and non‑technical audiences through executive‑ready narratives, visualizations, and recommendations. Influence senior stakeholders by connecting AI outputs to decisions, actions, and measurable R&D or Medical outcomes. What you’ll bring: Experience & Domain Expertise 6–10 years of experience in data science, advanced analytics, or applied AI, with at least 2 years in a people‑ or workstream‑leadership role within the life sciences industry. Demonstrated experience applying analytics and AI in pharmaceutical or biotech R&D, with exposure to Clinical Development and/or Medical Affairs strongly preferred. Strong understanding of regulated R&D environments, including data quality, validation, traceability, and compliance considerations. Technical & Analytical Skills Deep hands‑on experience with statistical and machine learning methods, including regression, classification, clustering, and predictive modeling. Proficiency in Python and/or R, with strong working knowledge of SQL; experience with common ML and data science frameworks (e.g., TensorFlow, PyTorch, scikit‑learn) preferred. Prior experience with statistical programming tools such as R, SAS, Python, MATLAB, or equivalent, and the ability to select appropri