Associate Director, Clinical AI Translation & Solutions

AstraZeneca•Cambridge, MA

12d•$144,649 - $216,973

About The Position

This role translates clinical workflows into safe, measurable AI systems that support clinical trials and improve patient outcomes. Working closely with clinical professionals and engineers, the Associate Director observes real-world operations, identifies appropriate AI opportunities, and turns those insights into software. The initial focus is agentic workflows for clinical operations, with reusable patterns expected to extend across Clinical Development. The position owns clinical AI workflows from discovery through deployment, adoption, and benefit measurement. It combines hands-on prototyping and software delivery with workflow design, evaluation, human-in-the-loop testing, data governance, compliance, and cross-functional communication. The role also promotes reusable enterprise capabilities, makes build/buy/partner decisions, and mentors scientists and engineers.

Requirements

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.
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 about clinical, operational, and regulatory constraints.
Experience evaluating AI systems using technical metrics and expert judgment, including situations 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 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.

Nice To Haves

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, including 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 inform release decisions.
Publications, patents, open-source contributions, or a record of shipping technically differentiated AI products.

Responsibilities

Own a portfolio of clinical AI workflows from discovery through deployment, adoption, and measurable benefit realization, turning early wins into platform capabilities.
Observe clinical research associates, site-monitoring leaders, clinical operations, and medical stakeholders; translate pain points into product requirements, data contracts, acceptance criteria, and evaluation plans.
Build and iterate prototypes in Python; contribute production code, review pull requests, inspect logs, reproduce failures, and collaborate with engineers to resolve issues.
Design agentic workflows that combine language models, deterministic services, enterprise tools, and human review, with clear evidence provenance, uncertainty handling, and fail-safe behavior.
Distinguish demonstrations from production releases and non-GxP workflows from regulated use, making transparent risk and lifecycle trade-offs.
Define measures of technical performance, clinical usefulness, workflow quality, user adoption, cycle time, and operational impact.
Lead testing with clinical experts and convert feedback into prioritized, testable product changes that increase trust and adoption.
Partner with data-product owners and platform teams to establish reliable access to clinical systems, documents, and structured data while protecting privacy and integrity.
Work with Quality, Regulatory, Privacy, Security, and R&D IT to incorporate GCP, GxP, validation, auditability, and lifecycle requirements from the outset.
Promote reuse of enterprise platforms, data products, agent components, and evaluation assets; determine when to build, buy, partner, or adapt.
Communicate product decisions, evidence, risks, dependencies, and trade-offs to technical teams, clinical leaders, and executives.
Mentor scientists and engineers and raise standards for product thinking, technical execution, and clinical partnership.