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 engaged in setting strategy and direction for data science and AI within R&D. I play a key role in helping shape the approach to data that is emerging across R&D and influencing our data strategy across the enterprise. I am also 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.

As a particle physicist by training, I have spent my early career as an academic researcher obtaining my doctorate in High Energy Particle Physics from the University of Manchester, before embarking on a 3-year postdoctoral fellowship studying the subtle differences between matter and antimatter. Upon leaving academia, I joined Tessella as a scientific software consultant, working directly as a lead software engineer and data scientist on many large-scale projects across the consumer products, petrochemicals, life sciences, automation and public sectors.

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.

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 Chief Data Scientist, BioPharmaceuticals R&D, AstraZeneca

CURRENT ROLE

Chief Data Scientist, BioPharmaceuticals R&D, AstraZeneca

2021

Ranked 4th most influential data and analytics practitioner by DataIQ

2020

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

2019

Honorary Reader in Computer Science at the University of Manchester

  Featured publications

Using Machine Learning to Individualize Treatment Effect Estimation: Challenges and Opportunities

Curth, A, Peck, RW, McKinney, E et al. Clinical Pharmacology & Therapeutics. 2023. DOI: http://doi.org/10.1002/cpt.3159

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. http://rd.springer.com/article/10.1007/s10994-019-05795-1

Distinguishing prognostic and predictive biomarkers: an information theoretic approach.

Sechidis K, Papangelou K, Metcalfe P et al. Bioinformatics. 2018. 34(19): 3365–3376 http://academic.oup.com/bioinformatics/article/34/19/3365/4991984

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 http://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-016-0727-y

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

Weatherall J. Trials. 2015 16; 152. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4660086/

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. http://www.ncbi.nlm.nih.gov/pubmed/23920514

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. http://journals.sagepub.com/doi/abs/10.1177/0092861511428890

Veeva ID: Z4-66096
Date of preparation: June 2024