Editorial Board
Sophie Carr (chair)
I am the founder and owner of Bays Consulting. I trained as an engineer and took a PhD in Bayesian belief networks, and have worked in data analytics ever since. Or to put it another way, I have made a living out of finding patterns. I am the vice-president for education and statistical literacy at the RSS, officially one of the World’s Most Interesting Mathematicians and was a member of the first cohort of data scientists to achieve the new, defined standard of professionalism award from the Alliance for Data Science Professionals.
I am delighted to be chairing the editorial board of the new data science project from the RSS and am excited to be a part of this project as it evolves into a key resource for all data science practitioners and leaders. To make this a place that helps everyone learn and develop within this field, I’d like to encourage all practitioners, no matter what stage of their career, to submit the type of resource they learn best from (whether that be an article, some code, a dataset, a case study or a problem/exercise to solve) on a topic that is important to them – from ethics to analysis plans through to tips on how code. Whatever it is you’re working on that you care about, I’d like to ask you to become an active part of the wonderful community of data scientists by sharing your knowledge.
Yaxin Bi
I am a reader in the Artificial Intelligence Research Centre, School of Computing at Ulster University, UK. My research interests include machine learning, data science and ensemble methods underpinned with the Dempster-Shafer theory of evidence. I have led on developing data analytics for anomaly/change detection within satellite data, as well as text analytics for document categorisation and sentiment analysis. I have served a number of international conferences as general chair and technical program committee chair. I am an associate editor for the International Journal of Intelligence Systems and a core steering committee member for the series of International Conference on Knowledge Science, Engineering and Management.
As a member of this editorial board, I am passionate about contributing my expertise to the development of data science and its real-world applications. I look forward to sharing my experience with others in the data science community, in particular to address ethical challenges posed by large language models and to inspire new curriculum development in the field of data science.
Lee Clewley
I am head of applied AI in GSK’s AI and Machine Learning Group, R&D. I began my career as an astrophysicist, initially working out the mass of our galaxy, before pondering the bigger universe. After six years at Oxford as a post-doc lecturer publishing in theoretical cosmology, I entered the very real world of manufacturing at GSK. For the first five years I applied statistical modelling techniques across manufacturing, such as the first end-to-end continuous manufacturing prototype for tablets. The past decade has been spent as a lead data scientist delivering high value projects across R&D and manufacturing.
I joined this editorial board because the impulse to assemble and present complex data science ideas to a wide range of folks has never left me. I have been a data scientist leader since it became a distinct profession but also have a decent understanding of classical and modern predictive analytics tools and statistics. I have spent a good deal of my adult life teaching students and non-technical adults alike.
I am passionate about delivering useful, pragmatic data science ideas and products to a wide range of people. I enjoy trying to communicate complex scientific information simply. Alongside my peers in the team, I want to support and develop data scientists at whatever stage in their career. I want to help cut through the hype and nonsense to give the best advice possible in a highly respected institution like the RSS.
Jonathan Gillard
I am a professor at the School of Mathematics, Cardiff University, where I am also research group lead for statistics. I have a history of publications in statistical methods and an interest in the theoretical underpinnings of data science, but I have also worked with industry on applied and practical projects. Recent industrial partners of mine include the Office for National Statistics (ONS) and the National Health Service, on projects such as anomaly detection and understanding heterogeneity. Indeed, I am academic chair for Cardiff University’s strategic partnership with the ONS which serves to spur and catalyse collaboration between both organisations.
I am excited to see what this site can achieve. I’m particularly keen to support articles describing the latest, cutting-edge methodology, as well as contributions from data professionals in industry who can explain how data science has managed to offer insights into important problems. Data science is a broad church and I want to ensure that the full array of work in this area is represented on this site. I think the diversity of the editorial board will help promote this objective.
Willis Jensen
I’m a statistician and leader with over 20 years of experience applying data science methods to real-world challenges in business, quality, product development, manufacturing, and supply chain. I hold a PhD in statistics from Virginia Tech and an MS in statistics from Brigham Young University (BYU).
Currently, I lead a business intelligence team at CHG Healthcare in the USA and serve as an Adjunct Professor of Statistics at BYU, where I get to share my enthusiasm for data with students. A long-time member and volunteer for the ASA, I represent the association on the Real World Data Science board.
Throughout my career, I’ve built and led high-performing teams, developed data-drive solutions, and helped organizations become more analytically mature. While I enjoy the technical depth of statistical modeling, I’m most passionate about using data to solve meaningful business problems. With over 25 publications focused on practical applications, I specialize in bridging the gap between business and the broad tent of data science—helping leaders ask better questions and act on insights.
Monnie McGee
I received my B.A. in Mathematics and English from Austin College and completed my M.A. and Ph.D. in Statistics at Rice University. My primary research interests lie in the statistical analysis of high-throughput biological data, with emphasis on the development and application of methods for background correction, normalization, and inference procedures tailored to the unique data structures arising in technologies such as RNA-Seq, microarrays, and flow cytometry.
I have extensive expertise in the analysis of data constrained to the simplex, and my methodological contributions include the development of inference procedures for compositional data using the Nested Dirichlet Distribution. In parallel, I maintain an active research agenda in sports analytics, particularly for event-driven sports including track and field, competitive diving, swimming, and gymnastics. My work in this area focuses on ranking models, performance trajectories, and judge reliability.
Most recently, I have extended my research to include applications of text analytics and natural language processing for comparing responses from various generative AI models in the context of statistical reasoning.
Detlef Nauck
I am a BT Distinguished Engineer and the head of AI and data science research for BT’s Applied Research Division located at Adastral Park, Ipswich, UK. I have over 30 years of experience in AI and machine learning and lead a programme spanning the work of a large team of international researchers who develop capabilities underpinning future AI systems. A key part of this work is to establish best practices in data science and machine learning, leading to the deployment of responsible and auditable AI solutions that are driving real business value.
I am a computer scientist by training and hold a PhD and a Postdoctoral Degree (Habilitation) in machine learning and data analytics. I am also a visiting professor at Bournemouth University and a private docent at the Otto-von-Guericke University of Magdeburg, Germany. I have published 3 books and over 120 papers, and I hold 15 patents and have 30 active patent applications.
I am passionate about promoting best practice in data science and believe that in the UK the RSS is the ideal professional body to pursue this goal. For me, Real World Data Science is an opportunity to share my experience and inspire a new generation of data scientists.
Debisree Ray
I am a Data Scientist at U.S. Steel / Big River Steel (acquired by Nippon Steel), where I develop data-driven solutions that span statistical analytics, visualization, predictive modeling, LLM-backed systems, and interactive dashboards to optimize steel manufacturing processes, enhance quality, and reduce losses. I hold a Ph.D. in Computational Nuclear Structure Physics and I am passionate about AI-driven product innovation within industrial and enterprise settings. Beyond my professional role, I love collaborating with researchers in astrophysics/astronomy and nuclear structure theory to apply advanced ML/DL methodologies to academic scientific problems.
Fatemeh Torabi
I am a research officer and data scientist at Health Data Research UK and a fellow of the RSS. My background is in mathematical statistics and health data science, and my research interests span novel analytical and computational methods for statistical inference in panel data and population health. I am supporting the development of the Real World Data Science platform in the context of health with a specific focus on how health data can be harnessed through data linkage and analysis to answer important questions and improve the lives of our population.
Isabel Sassoon
I am a senior lecturer in computer science (data science) at Brunel University and the programme lead for the data science and analytics MSc programme. My research interests are in data-driven automated reasoning and its transparency and explainability, which brings in data science and artificial intelligence with applications within the health space. I am also championing open science and reproducible analysis in both my research and teaching. I have a PhD in informatics from King’s College London and it was on the topic related to the use of AI to support statistical model selection. Prior to Brunel I was a teaching fellow and research associate at King’s College London and before that I worked for more than 10 years as a data science consultant in industry, including 8 years at SAS UK.
I have been working, researching, consulting and teaching in the data science space for a while and I am passionate about the domain and its applications. I am always interested in sharing and hearing what else is being done to support, inform and inspire all those studying and working in the field of data science. I look forward to sharing case studies, how-to guides and data science profiles through this website.
Past team members
Brian Tarran
Brian is the founding editor of Real World Data Science and former head of data science platform for the Royal Statistical Society (RSS). He worked for the RSS from 2014 to 2024, initially as editor of Significance Magazine, a joint publication of the RSS, the American Statistical Association and the Statistical Society of Australia. He launched Real World Data Science in October 2022. He is a former editor of Research-Live.com and was launch editor of Impact magazine, both published by the Market Research Society.