Associate Director, Clinical AI Translation & Solutions

AstraZeneca; Kendall Square (MA); On-site; USD 210,000 - 320,000; Full time

Job summary

AstraZeneca seeks a Director, AI Research to lead the design and delivery of clinical AI workflows. You will translate clinical operations into AI-enabled solutions, build hands-on prototypes in Python, and guide cross-functional teams through evaluation, deployment, and adoption.

You will work with clinical professionals and engineers to ensure scalable, compliant, and measurable AI impact across trials and operations.

Qualifications

• PhD or MD or equivalent with strong computational track record.
• 4 to 7 years post-PhD experience in applied AI/ML in healthcare or life sciences.
• Hands-on coding in Python and SQL with modern development practices.
• Experience building or deploying LLM-based or agentic systems.

Responsibilities

• Portfolio Ownership: Own a portfolio of clinical AI workflows from discovery through deployment, adoption, and measurable benefit realization.
• Workflow Discovery and Design: Observe clinical stakeholders to convert pain points into product requirements and evaluation plans.
• Hands-on Prototyping and Delivery: Build prototypes in Python; contribute production code and review PRs with engineers.
• Agentic Workflow Design: Combine language models, deterministic services, and enterprise tools with risk-aware design.

Skills

PhD in Computer Science

Python and SQL

LLM‑based agentic systems

Stakeholder communication

Education

PhD in Computer Science

MD or master's degree

Job description

Job Title: Director, AI Research

Introduction to role:

Are you ready to turn complex clinical workflows into safe, measurable AI systems that accelerate trials and meaningfully improve patient outcomes? This role sits at the heart of how we design, conduct, monitor, and analyze clinical studies-translating real-world operations into AI solutions that scale across the enterprise and deliver tangible business value. You will work side-by-side with clinical professionals and engineers to observe how work truly happens, identify where AI can safely help, and translate that understanding into working software. Do you thrive on building hands‑on prototypes, making disciplined scope decisions, and staying accountable through evaluation, deployment, adoption, and iteration? Your initial focus will include agentic workflows for clinical operations, with the expectation that reusable patterns will extend across Clinical Development.

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 mon