Associate Director, Clinical AI Translation & Solutions

Accountabilities

• Portfolio Ownership: Own a portfolio of clinical AI workflows from discovery through deployment, adoption, and measurable benefit realization, ensuring early wins become platform capabilities.
• Workflow Discovery and Design: Observe CRAs, site-monitoring leaders, clinical operations, and medical stakeholders to convert pain points into product requirements, data contracts, acceptance criteria, and evaluation plans.
• Hands-on Prototyping and Delivery: Build and iterate prototypes in Python; contribute production code, review pull requests, inspect logs, reproduce failures, and collaborate with engineers to resolve them.
• Agentic Workflow Design: Combine language models, deterministic services, enterprise tools, and human review with clear evidence provenance, uncertainty handling, and fail-safe behavior.
• Scope and Release Discipline: Separate demonstrations from production releases and distinguish non-GxP workflows from regulated use, making transparent risk and lifecycle trade-offs.
• Outcome Measurement: Define outcome measures spanning technical performance, clinical usefulness, workflow quality, user adoption, cycle time, and operational impact.
• Human-in-the-Loop Testing: Lead testing with clinical experts and translate feedback into prioritized, testable product changes that increase trust and adoption.
• Data Access and Governance: Partner with data-product owners and platform teams to establish reliable access to clinical systems, documents, and structured data while protecting privacy and integrity.
• Compliance by Design: Collaborate with Quality, Regulatory, Privacy, Security, and R&D IT to embed GCP, GxP, validation, auditability, and lifecycle requirements from the outset.
• Reuse and Build/Buy Decisions: Promote reuse of enterprise platforms, data products, agent components, and evaluation assets; identify when to build, buy, partner, or adapt.
• Communication and Influence: Communicate product decisions, evidence, risks, dependencies, and trade-offs clearly to technical teams, clinical leaders, and executives.
• Mentorship and Standards: Mentor scientists and engineers and raise the team’s standard for product thinking, technical execution, and clinical partnership.

Essential Skills/Experience

• PhD in Computer Science, Machine Learning, Statistics, Biomedical Informatics, Computational Biology, or a related quantitative discipline; or an MD, master’s degree, or equivalent experience supported by a strong computational track record.
• 4 to 7 years of post-PhD (or equivalent) experience delivering applied AI/ML or data products, including experience in healthcare, life sciences, clinical research, or another high-consequence domain.
• Current, hands-on coding ability in Python and SQL, with experience using version control, tests, APIs, data pipelines, and modern cloud or containerized development environments.
• Experience building or deploying LLM-based or agentic systems, including retrieval, tool use, structured outputs, workflow orchestration, and human review.
• Demonstrated ability to translate ambiguous stakeholder needs into a bounded product, working prototype, measurable acceptance criteria, and delivery plan.
• Ability to lead workflow-discovery and design sessions with clinical professionals and communicate credibly without oversimplifying clinical, operational, or regulatory constraints.
• Experience evaluating AI systems using both technical metrics and expert judgment, including cases where ground truth is noisy, incomplete, or context dependent.
• Strong understanding of trustworthy AI practices, and how to design provenance, access control, and uncertainty handling directly into a working system.
• Excellent written and verbal communication across engineering, scientific, clinical, quality, and executive audiences.
• Evidence of leading cross-functional delivery through influence, including prioritization, conflict resolution, and shipping decisions under ambiguity.

Desirable Skills/Experience

• Direct experience with clinical trial conduct, site monitoring, risk-based monitoring, medical monitoring, clinical data review, or trial technology in a pharmaceutical, biotech, CRO, or health-system environment.
• Working knowledge of GCP and GxP computerized-system validation, and how to scope a feature for regulated versus non-regulated use before it is built.
• Experience with clinical systems and data such as CTMS, EDC, eTMF, safety systems, EHR, monitoring reports, or clinical data platforms.
• Experience designing reusable product capabilities across multiple therapeutic areas, studies, or business units.
• Familiarity with observability, LLMOps/MLOps, and production monitoring for deployed AI systems, and how evaluation results feed release decisions.
• Publications, patents, open-source contributions, or a record of shipping technically differentiated AI products.