AstraZeneca
Associate Director, Clinical AI Translation & Solutions
$145K–$217K
US - Cambridge Kendall SQ - MA · Director · Full-time · Posted Oct 15, 2026
- Experience
- 4 to 7 years of post-PhD (or equivalent) experience.
- Education
- 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.
About the job
Leads discovery, development, evaluation, and deployment of clinical AI workflows, translating clinical operations needs into safe, measurable, reusable software.
What you'll do
- Own clinical AI workflows from discovery through deployment, adoption, and benefit measurement.
- Prototype and deliver AI systems with engineers, including agentic workflows with human review and fail-safe behavior.
- Partner with clinical, data, quality, regulatory, privacy, security, and IT teams on evaluation, governance, and compliant delivery.
What they ask for
- PhD in 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 in healthcare, life sciences, clinical research, or another high-consequence domain.
- Hands-on Python and SQL skills, with experience using version control, tests, APIs, data pipelines, and modern cloud or containerized environments.
- Experience building or deploying LLM-based or agentic systems, including retrieval, tool use, structured outputs, workflow orchestration, and human review.
Nice to have
- Direct experience with clinical trials or related pharmaceutical, biotech, CRO, or health-system work.
- Working knowledge of GCP and GxP computerized-system validation.
Pay
$144,648.8 to $216,973.2 USD (base salary)
Tools
Python, SQL, LLM-based systems, APIs, CTMS, EDC, eTMF, EHR
AI focus
Build and deploy clinical AI and agentic workflows, with human review, trustworthy AI safeguards, evaluation, and measurable clinical and operational outcomes.
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