AstraZeneca is looking to turn clinical operations data into action to deliver 20 new medicines by 2030 while reducing ~$300M in waste
Requirements
- Advanced hands-on with Python and visualization (Power BI, Spotfire)
- 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
- 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
Other
- Bachelor’s degree in computer science, data/analytics, statistics, engineering, or related field; 3+ years’ experience
- Clear written and verbal English; able to explain assumptions, uncertainties, and limitations
- Critical thinking, a growth mindset, grit, and resilience
- Mentoring peers through high-quality delivery
- Ability to work in a hybrid environment
