Senior Specialist, Data Scientist

AstraZeneca • Durham, NC

1d • Hybrid

About The Position

Join the Operational Data Strategy (ODS) team to turn clinical operations data into action and help AstraZeneca deliver 20 new medicines by 2030 while reducing ~$300M in waste; you’ll develop advanced analyses and machine‑learning models (dashboards in Power BI are for storytelling, not the core output) that power end‑to-end aggregated planning—one unified view that sharpens prioritization, flags risks earlier, accelerates trade‑offs, and aligns clinical demand and supply; reporting to the Strategic Analytics & Enablement Lead, you’ll shape how R&D data is collected, organized, validated, and analyzed, driving portfolio‑wide transparency and evidence-based decisions while exemplifying critical thinking, a growth mindset, grit, and resilience, and mentoring peers through high‑quality delivery.

Requirements

• Bachelor’s degree in computer science, data/analytics, statistics, engineering, or related field
• 3+ years’ experience
• Advanced hands-on with Python and visualization (Power BI, Spotfire); track record delivering advanced analytics, not just dashboards
• Proficient with SQL/NoSQL, ETL, cloud platforms, and software best practices (reproducibility, version control)
• Proven complex analysis in business/scientific domains, including Clinical Operations
• Strong grasp of data science, ML algorithms, statistical inference, and model evaluation
• Clear written and verbal English; able to explain assumptions, uncertainties, and limitations

Nice To Haves

• Experience in Agile delivery and exposure to modern MLOps
• Evidence of improving processes, documentation, quality standards, and driving stakeholder adoption

Responsibilities

• Deliver scalable, well-governed analytics: translate operational needs into robust analyses and ML models; use dashboards for storytelling.
• Own the end-to-end workflow: frame hypotheses, source and prep data (DBs/APIs/files), run exploratory/descriptive/predictive analyses, and present clear, actionable insights.
• Ensure quality and speed: apply reviews and source checks, state assumptions/limits, stay in scope, and respond to ad hoc requests with timely outputs.
• Partner and enable work with data engineering on ETL, train users, share best practices, and build adoption of ODS analytics.
• Grow and lead by example: stay current on methods/tools and model integrity, initiative, adaptability, organization, and strategic thinking.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees