Head of Clinical Data Science — Vaccines & Immune Therapies (V&I)

AstraZeneca

22h•Hybrid

About The Position

At AstraZeneca, this is where bold science meets real world impact. In Vaccines & Immune Therapies, we turn cutting edge ideas into transformative medicines for some of the world’s most complex diseases. We directly impact the lives of populations around the world, by turning our ambition to prevent disease into a reality. With an agile mindset and the strength of a global brand, we challenge and change how things are done. We’re building an organisation that resets expectations of what a biopharmaceutical company can be—opening new ways to work, pioneering novel methods, and bringing unexpected teams together. Our Clinical Data Science team partners directly with clinical, biometrics and science teams across research and development. We apply machine learning, large language models, predictive modelling and advanced visualisations to accelerate development end to end. Now, we’re looking for a Head of Data Science to lead this team and the Therapeutic Area data science strategy. Accountabilities: As Head of Data Science for V&I, you will define the data science strategy for the TA and lead delivery of a high impact portfolio spanning clinical and operational/business data. You will set direction, inspire and develop a team of data scientists, and act as the TA’s senior spokesperson for data science—advising leaders, shaping investments, and ensuring our work translates into clear, actionable insights for the business and for patients. You will cultivate strong partnerships across Research & Development, IT, Medical and Commercial as well as other data science groups, and manage external collaborations with vendors and academic partners. You’ll also champion scientific accuracy and software engineering best practices to ensure robust, reproducible, and scalable delivery.

Requirements

PhD or MSc in a relevant discipline (e.g., statistics, computer science, mathematics, engineering, physics) or equivalent experience.
Proven data science leadership with end to end delivery: data collection and preprocessing, modelling for business and clinical problems, and decision oriented data visualisation.
Significant experience in drawing conclusions from analysis and presenting analysis results in a succinct fashion to senior stakeholders, along with recommended actions
Knowledge of data analytic and visualization tools such as R, Python, Spotfire or Tableau.
Experience working with database systems (eg SQL, NoSQL, graph)
Proven experience in leading and managing colleagues
Experience in influencing and controlling budgets
Excellent written and verbal communication skills

Nice To Haves

Master’s degree in a quantitative field such as mathematics, computer science, engineering, physics, statistics, or economics.
Life sciences or healthcare experience, especially within clinical development or biometrics.
Experience operating in high performance computing or cloud environments.
Success in a complex, global organisation.
Experience in data mining, machine learning, large language models and/or operations research; strong fundamentals in statistics and algebra.
Demonstrated problem solving aptitude and bias to action.

Responsibilities

Set the TA strategy: Define and deliver the V&I data science strategy across clinical and operational/business domains, aligning to TA goals and strategy.
Grow impact and pipeline: Identify and prioritise new applications of data science in V&I; translate business problems into analytics questions and deliver solutions that inform decisions and predict outcomes.
Lead a high performing team: Line manage and provide inspirational leadership to data scientists at varied career stages; cultivate an inclusive, learning oriented culture.
Be the TA’s data science voice: Serve as key expert and spokesperson; advise senior stakeholders on innovative analytics, generative AI, and related technologies, clearly communicating insights, uncertainty, and limitations.
Elevate standards: Drive adoption of best practices in model development, MLOps, validation, documentation, and data governance; define and implement TA specific training.
Partner and influence: Build effective collaborations across internal data science groups; shape and manage external partnerships and vendors to deliver value at speed and scale.
Steer investments: Influence budgets and resourcing to maximise portfolio outcomes; contribute to and lead strategic initiatives across the TA.
Represent the function: Share AZ’s work at internal forums and external conferences, strengthening our scientific presence.

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