Director, Applied Intelligence

Role SummaryAs Director of Applied Intelligence, you will report to the Senior Director, Applied Intelligence and lead delivery of rapid, high-impact AI/ML prototypes that inform enterprise investment and scaling decisions. You will be a lead on a high-velocity, elite team purpose-built to do one thing exceptionally well: take the hardest, most ambiguous AI/ML problems and rapidly determine whether they are solvable, how they should be solved, and what it will take to make them real at scale.You will de-risk high-impact AI/ML investments through fast, disciplined experimentation. This is not a sandbox.

Every proof of concept you produce will be built with the engineering hygiene of production code, because the best prototypes become the foundation of enterprise systems. Your work will directly shape what gets scaled, what gets killed, and where the organization invests next.You will operate at the intersection of AI innovation, engineering discipline, and business urgency. You will be the first call when something matters and nobody knows the answer yet.Role ResponsibilitiesTechnical Delivery and Team Leadership Lead day-to-day technical delivery of applied AI projects, participating in architecture, code reviews, and prototyping Mentor and develop individual contributors, set team standards, and recruit talent to accelerate delivery* Maintain hands-on involvement in modeling, data pipelines, and integration work to preserve technical credibilityRapid Prototyping and Experimentation Run 2 to 6 week prototype cycles: define scope, design experiments, deliver evaluation metrics, and recommend clear go/no-go decisions* Translate ambiguous commercial problems into testable hypotheses and rapid experiments that produce actionable results* Remain deeply hands-on in AI/ML engineering and prototypingEngineering Hygiene and MLOps Ensure prototypes follow robust engineering practices: version control, CI/CD for models, containerization, reproducibility, automated tests, and clear documentation* Apply MLOps principles for deployment-ready artifacts, including model monitoring and rollback strategies appropriate for regulated environments* Promote modular, reusable code and extraction of common patterns to reduce reworkPartnership Prepare validated prototypes for structured handoff to industrialization, production, or IT teams with runnable artifacts, runbooks, and acceptance criteria* Collaborate closely with partner Analytics, Data Science, and IT teams to ensure prototypes align with enterprise architecture and standards* Support scaled rollouts by providing implementation support during transition from build to operationsStakeholder Communication Communicate prototype results, limitations, and recommended next steps to the Senior Director, product owners, and business stakeholders in clear, outcome-oriented terms* Deliver concise executive summaries and data-backed recommendations to inform prioritization and funding decisionsBasic Qualifications 8+ years of experience in AI/ML, data science, or applied research with substantial hands-on technical delivery with a BA/BS, 7+ years with an MS/MBA or 5 years with a PhD* Active proficiency in Python and modern ML frameworks; regular coding and technical leadership in production or prototype contexts* Strong practical experience with MLOps, model lifecycle management, CI/CD concepts, containerization, and reproducibility tooling* Demonstrated ability to design and deliver scalable AI/ML solutions while operating in fast, resource-constrained prototype environments* Proven track record applying AI/ML to commercial functions in pharmaceutical or life sciences settings, delivering measurable business impact such as improved forecasting, segmentation, or real-world evidence analytics* Excellent stakeholder communication skills and experience presenting results and recommendations to senior leaders* Experience working in regulated industries such as pharmaceutical, biotech, medical devices, or financial servicesPreferred Qualifications Advanced degree (M.S. or Ph.D.) in Computer Science, Statistics, Computational Biology, Engineering, or related quantitative field* Prior experience leading prototype teams, innovation labs, or applied AI delivery teams inside large enterprises* Familiarity with cloud-agnostic ML deployment patterns and production practices, including model deployment, monitoring, container orchestration, and enterprise data governance suitable for regulated environments* Experience creating reusable engineering patterns, templates, and runbooks for broader organization use* Track record of hiring and developing senior individual contributorsWork Location Assignment: HybridLast Date to Apply for Job: 5/5/2026The annual base salary for this position ranges f