Senior Specialist, Data Scientist at AstraZeneca

Durham, North Carolina, United States -

Full Time

Start Date

Immediate

Expiry Date

22 Mar, 26

Salary

0.0

Posted On

22 Dec, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Power BI, SQL, NoSQL, ETL, Cloud Platforms, Machine Learning, Statistical Inference, Data Science, Data Analytics, Clinical Operations, Agile Delivery, MLOps, Data Visualization, Critical Thinking, Communication

Industry

Pharmaceutical Manufacturing

Description

The Role 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. In this role, you will 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. Education, Qualifications, Skills and Experience Essential 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. Desired Experience in Agile delivery and exposure to modern MLOps. Evidence of improving processes, documentation, quality standards, and driving stakeholder adoption. Are you interested in working at AZ, apply today! AstraZeneca is an equal opportunity employer that is committed to diversity and inclusion and providing a workplace that is free from discrimination. AstraZeneca is committed to accommodating persons with disabilities. Such accommodation is available on request in respect of all aspects of the recruitment, assessment and selection process and may be requested by emailing AZCHumanResources@astrazeneca.com. #LI-Hybrid Date Posted 20-Dec-2025 Closing Date 04-Jan-2026 Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and employees. In furtherance of that mission, we welcome and consider applications from all qualified candidates, regardless of their protected characteristics. If you have a disability or special need that requires accommodation, please complete the corresponding section in the application form.

Responsibilities

You will develop advanced analyses and machine-learning models to enhance clinical operations data. This includes delivering scalable analytics, owning the end-to-end workflow, and ensuring quality and speed in outputs.