Senior Specialist, Data Scientist

Role Summary

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 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.

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.

Qualifications

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