Associate Director, Real World Evidence Data Scientist, Gaithersburg, MD

AstraZeneca•Gaithersburg, MD

7d•Hybrid

About The Position

The Centre for Oncology Data Excellence (CODE) is a function focused on delivering best-in-class, scientifically rigorous research to support Global Medical Evidence Generation. Within CODE, the Real World Evidence (RWE) Data Scientist will be part of the Oncology Data & Analytics (ODA) team, responsible for real-world data (RWD) analysis, Data Foundation & Governance, Analytics Platforms/Tools, and innovative AI capabilities. This role involves collaboration across multiple functions within Medical Evidence Generation. The ideal candidate will have a proven track record of delivering value through the utilization of routinely collected data from healthcare settings, providing health analytics and insights in various contexts including Public Health, Pharmaceutical Research and Development, and Commercial/Payer sectors. They will collaborate with colleagues in Oncology Outcomes Research (O2R), Epidemiology, and Statistics to provide scientific and technical guidance on study design, data selection, best practices in using RWD, and implementing advanced statistical methods. The successful candidate will be expected to leverage cutting-edge AI tools, including GenAI, to scale analytics tasks and augment data insights.

Requirements

PhD or MS in epidemiology, biostatistics, data science, computer science, or related field such as health informatics with at least 3 years of relevant pharmaceutical industry or CRO experience.
Experience in RWE and familiarity with observational study methodologies.
At least 5+ years of experience working directly with large, complex healthcare datasets (claims/EHR/registries) in quantitative research.
Demonstrated proficiency in R/Python and SQL.
Expertise in developing FAIR-compliant data analytics pipelines supported by use of version control tools like GitHub and agile tools like JIRA.
Proven ability to build long-term and trusted partnerships with cross-functional stakeholders, manage conflicts and competing priorities to drive timely, high-quality outcomes.
Strong and effective communication skills, both oral and written.
Demonstrated ability to articulate research questions and translate them into structured data analysis plans and technical workstreams that deliver measurable business value.
Proficiency in applying GenAI-based coding assistants (e.g., GitHub Copilot) and agentic tools to support project planning, data analysis, code review, or scientific documentation workflows.
A curious learner who continuously deepens disease knowledge, RWD expertise and explores emerging technologies and RWE methodologies to meet evolving stakeholder needs.

Nice To Haves

Expertise in clinical data standards, medical terminologies, and controlled vocabularies used in healthcare data and ontologies (e.g., ICD-9/10, NDC, HCPCS).
Ability to lead and manage cross-functional data science projects with track record of peer-reviewed publications and/or regulatory submissions.
Demonstrated experience with building production grade analytics solutions using tools such as R (Shiny) or Python (Dash).
Excellent organizational and project management skills with demonstrated ability to prioritize and manage multiple tasks.
Experience with AI-empowered software and web application development in pharmaceutical, biomedical, or healthcare sectors.

Responsibilities

Collaborate with key stakeholders, including outcomes research and next-gen science groups to support study design, conduct RWD feasibility assessments, and execute protocol-driven and insight projects to a high standard.
Maintain in-depth knowledge of real-world oncology data sources (claims, EHR, and registries).
Provide strong insights into the strengths and limitations of in-house data sources to facilitate data suitability assessments for study planning.
Translate ambiguous business and scientific questions into clear, actionable analytic specifications and programming tasks (R/Python/SQL).
Champion reusable code libraries, version control, and reproducible data science workflows.
Stay current with methodological developments in real world evidence (RWE) generation, including causal inference, ECA, ML/AI methods, and federated learning/OMOP, and provide clear technical input on study design and analysis.
Incorporate LLMs/GenAI, agentic workflows and AI coding tools into day-to-day workflows to accelerate code development, discovery, documentation, review, and insight generation.
Communicate complex methods and analysis results clearly to both technical and non-technical audiences internally and externally.