Associate Director, Real World Evidence Data Scientist

DataJobs

Gaithersburg (MD)

On-site

USD 145,000 - 217,000

Full time

2 days ago

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Benefits offered by this job

Short-term incentive bonuses

Equity-based awards for salaried roles

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Qualified retirement programs

Paid time off (vacation, holiday, and

Health, dental, and vision coverage

Job summary

AstraZeneca in Gaithersburg, MD is hiring a Real World Evidence Data Scientist to join the CODE and ODA team. This onsite role will shape analytic specifications and accelerate implementation using LLM and GenAI-enabled practices.

You will work with outcomes research and next-gen science groups to translate questions into R/Python/SQL tasks, develop data pipelines, and communicate results to diverse audiences.

Qualifications

PhD or MS in epidemiology, biostatistics, data science, computer science, or health informatics with at least 3 years of pharma/CRO experience.
Experience in real-world evidence (RWE) and observational study methodologies.
5+ years working directly with large, complex healthcare datasets (claims, EHR, registries) in quantitative research.
Proficiency in R, Python and SQL.
Experience building FAIR-compliant data analytics pipelines using GitHub and JIRA.
Ability to build long-term cross-functional partnerships and manage competing priorities.
Strong communication skills to translate research questions into data analysis plans.
Experience applying GenAI-based coding assistants (e.g., GitHub Copilot) and agentic tools.

Responsibilities

Partner with stakeholders to support study design and conduct RWD feasibility assessments.
Deliver protocol-driven and insight projects to a high standard.
Assess data sources (claims, EHR, registries) for study planning and data suitability.
Translate scientific questions into actionable analytic specifications using R/Python/SQL.
Promote reusable code, version control, and reproducible workflows.
Stay current on methods for RWE generation, including causal inference and ML/AI approaches.
Incorporate LLMs/GenAI and agentic workflows to accelerate coding and documentation.
Communicate complex methods and results clearly to technical and non-technical audiences.

Skills

Communication

Project management

Cross-functional collaboration

Documentation

Stakeholder engagement

Education

PhD or MS in epidemiology/biostatistics/data science/computer science or related field

Tools

Python

SQL

GitHub

JIRA

GitHub Copilot

OMOP

R Shiny

Python Dash

LLMs/GenAI

Job description

AstraZeneca is hiring a Real World Evidence Data Scientist to join the Centre for Oncology Data Excellence (CODE) and the Oncology Data & Analytics (ODA) team in Gaithersburg, Maryland. In this onsite role, you will support real-world data (RWD) analytics across the full workflow, from shaping analytic specifications to accelerating implementation with LLM and GenAI-enabled practices.

Working alongside stakeholders in outcomes research and next-gen science, you will translate scientific questions into programming tasks using R, Python, and SQL, while helping teams make data foundation and governance decisions for oncology studies. The role also includes staying current on RWE methodology, contributing technical input on study design and analysis, and communicating results to technical and non-technical audiences.

What you’ll do

Partner with key stakeholders, including outcomes research and next-gen science groups, to support study design and perform RWD feasibility assessments.
Deliver protocol-driven and insight projects to a high standard.
Use deep knowledge of real-world oncology data sources, including claims, EHR, and registries, to assess strengths, limitations, and data suitability for study planning.
Convert unclear business and scientific questions into actionable analytic specifications and programming tasks in R/Python/SQL.
Promote reusable code libraries, version control, and reproducible data science workflows.
Stay current with RWE generation methods, including causal inference, ECA, ML/AI methods, and federated learning/OMOP, and provide technical input for study design and analysis.
Incorporate LLMs/GenAI, agentic workflows, and AI coding tools in day-to-day work to accelerate code development, discovery, documentation, review, and insight generation.
Communicate complex methods and analysis outputs clearly to internal and external technical and non-technical audiences.

Required qualifications

PhD or MS in epidemiology, biostatistics, data science, computer science, or a 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.
5+ years working directly with large, complex healthcare datasets (claims/EHR/registries) in quantitative research.
Demonstrated proficiency in R/Python and SQL.
Expertise in building FAIR-compliant data analytics pipelines using version control tools such as GitHub and agile tools such as JIRA.
Proven ability to build long-term, trusted cross-functional partnerships, resolve conflicts, and manage competing priorities to deliver timely, high-quality outcomes.
Strong oral and written communication skills, including the ability to translate research questions into structured data analysis plans and technical workstreams that produce measurable business value.
Proficiency in applying GenAI-based coding assistants (e.g., GitHub Copilot) and agentic tools for planning, data analysis, code review, or scientific documentation workflows.
A curious learning mindset focused on deepening disease knowledge, RWD expertise, and emerging RWE technologies to meet evolving stakeholder needs.