Since joining AstraZeneca in 2007 I have held a number of diverse roles focused on driving the application of data science, advanced analytics and related approaches to unlock the full potential of data –transforming the way medicines are discovered and developed and making a difference to patients’ lives.

As a senior data science leader, I am focused on setting strategy and direction for data science and AI within R&D. I’m passionate about bringing out the best in our people and helping them to seize the opportunity to develop their careers doing something challenging and meaningful.

A particle physicist by training, I spent my early career as an academic researcher before becoming a scientific software engineer consulting across a range of different industries, including the life sciences.

I am an Honorary Reader in Computer Science at the University of Manchester. I serve on the Council of the Royal Statistical Society, where I am Vice-Chair of the Data Science Section. I have contributed to and published in a number of diverse fields such as data visualisation, cryptography, text mining, machine learning and health data science. In 2021, DataIQ ranked me at number four among their 100 most influential data and analytics practitioners.

What made me choose AstraZeneca – and what remains exciting to me over a decade later – is the opportunity to take the skills I have acquired as a data scientist and use them to develop medicines to make a real difference to patients’ lives.

Jim Weatherall Vice President, Data Science & AI, R&D


Vice President, Data Science & AI, R&D


Ranked 4th most influential data and analytics practitioner by DataIQ


Elected Council member for the Royal Statistical Society, starting January 2021


Honorary Reader in Computer Science at the University of Manchester

  Featured publications

Machine Learning for Clinical Trials in the Era of COVID-19.

Zame WR, Bica I, Shen C et al. Statistics in Biopharmaceutical Research. 2020. DOI: 10.1080/19466315.2020.1797867.

Efficient feature selection using shrinkage estimators.

Sechidis K, Azzimonti L, & Pocock A et al. Machine Learning. 2019. 1-26.

Distinguishing prognostic and predictive biomarkers: an information theoretic approach.

Sechidis K, Papangelou K, Metcalfe P et al. Bioinformatics. 2018. 34(19): 3365–3376

It’s a long shot, but it just might work! Perspectives on the future of medicine.

Wicks P, Hotopf M, Narayan V et al. BMC Medicine. 2016. 14: 176

Structured exploration of clinical trials data - finding the middle way.

Weatherall J. Trials. 2015 16; 152.

Taming EHR data: Using Semantic Similarity to reduce Dimensionality.

Kalankesh L, Weatherall J, Dhafari B et al. Studies in health technology and informatics. 2013. 192; 52-6.

Text Analytics for Surveillance (TAS): An Interactive Environment for Safety Literature Review.

Christensson C, Gipson G, Thomas T, Weatherall J. Drug Information Journal 2012. 46; 115-123.