Senior Director, Evidence Statistics, Real World Science & Analytics
AstraZeneca Spain - Barcelona Posted 13 July 2026
PharmaCRORegulatory AffairsQuality Assuranceheorbiostatisticscroinformaws
Job description
Location: Barcelona - Spain (3 days working from the office and 2 days working from home) Introduction to the role BioPharmaceuticals Medical (BPM) strives for the best patient outcomes by providing healthcare decision-makers around the world with the evidence and confidence they need on AstraZeneca therapies, as well as supporting our internal stakeholders with medical insight s and expertise . BPM provides medical leadership across the enterprise , including in support of life cycle planning and post-launch evidence generation . BPM aims to lead AstraZeneca in advancing its scientific leadership, being a great place to work, and keeping patients at the core of everything we do. The Senior Director, Evidence Statistics will play a key role in delivering statistical and methodological expertise to enable evidence generation activities, as well as developing and championing associated AI and analytic tools across the organization. The Senior Direc tor will lead a high - functioning Global statistics team that enable s the design, governance, interpretation and reporting of evidence generation studies across therapeutic areas . This role will be accountable for maintaining and maximizing the value of leading statistics expertise to support primary and secondary data studies . The Senior Director will also contribute to the Global Evidence organization’s efforts to identify , build, acquire , and embed AI tools and technologies that increase the efficiency and impact of evidence generation studies. W e are looking for a leader to nurture and maximize the impact of industry-leading statistical expertise for the benefit of patients around the world. The Senior Director will utilize their established technical leadership and the expertise of their team to drive impact across multi-disciplinary teams that include medical leads, epidemiologists , data scientists and other cross-functional partners to generate insights and evidence that informs clinical practice. Accountabilities Lead, develop, and inspire a high-performing team of statisticians supporting real-world evidence generation across therapeutic areas. Provide strategic and operational leadership for statistical input into evidence generation plans, study design, samp le size estimation, study governance , interpretation, and dissemination. Partner effectively with therapeutic area evidence leaders and key stakeholders across Medical, Market Access, HEOR, Data Science, and R&D to align statistical approaches with study objectives and evidence needs. Builds strong cross-functional collaboration with partner teams, such as Real World Data Science and the Real World Data Office Set clear roles, expectations, and accountabilities for team members, ensuring effective deployment of statistical expertise across the portfolio. Advance the use of innovative and fit-for-purpose statistical approaches to address complex evidence questions, including comparative effectiveness, causal inference, and value demonstration. Maintain deep awareness of emerging methodologies, related AI advancements, regulatory expectations, and external best practice in the design, analysis, and interpretation of real-world evidence . Lead continuous improvement and capability-building initiatives that strengthen quality, consistency, efficiency, and methodological excellence across the statistics function. Represent the function externally through engagement with scientific experts, professional societies, collaborators, and other external partners to bring in and shape leading practice. Ensure appropriate statistical support, prioritization, and resourcing across the portfolio to deliver high-quality evidence to agreed timelines. Coach, mentor, and develop statistical talent, building both technical depth and leadership capability within the team. Drive the development and adoption of tools, standards, and ways of working that improve the efficiency , reproducibility, and impact of evidence generation, including appropriate AI-enabled and di
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