Real World Data Science - Meet the team

Real World Data Science - Meet the team

Real World Data Science
Editor
Brian Tarran
I am a writer and editor with 20 years of experience covering the research and data space. I have worked for the Royal Statistical Society (RSS) for the past 8 years, and was editor of Significance Magazine (a joint publication of the RSS, the American Statistical Association and the Statistical Society of Australia) prior to the launch of Real World Data Science. I am a former editor of Research-Live.com and was launch editor of Impact magazine, both published by the Market Research Society.
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 data set, 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.
Sayma Chowdhury
I am a freelance data scientist on Upwork, with a client portfolio ranging from start-ups to commercial businesses such as supermarkets, pharmaceuticals and automotive manufacturers. I transitioned into data science from law in 2017, having completed a MicroMasters in statistics and data science with MIT and a Professional Certificate in data science with Harvard University. In advance of a PhD in digital humanities, I am currently completing a MicroMasters in data, economy and development policy with MIT and an MSc in data science with the University of Aberdeen. My research interests are in text analytics, natural language processing and machine learning.
The RSS was instrumental in my training and professional development as a data scientist in the early stages of my career, particularly in mastering statistics and R. Data science is a rapidly growing field with employment opportunities in many sectors but there is an increasing need to uphold a realistic and accurate expectation of competency within the industry. I will endeavour to present expert practical guidelines for data scientists as well as demonstrate the versatility of the profession. I hope the site will be a benchmark for academic and professional resources by expert data scientists from industry, accessible to data scientists at all levels, anywhere in the world.
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.
Juhi Gupta
I am a lecturer in health data sciences and the deputy programme director of the health data science MSc in the School of Health Sciences, University of Manchester (UoM). I have a background in genetics, pharmacology and bioinformatics, and my doctoral thesis focussed on multi-omics data analysis using machine learning methods for precision medicine. I have worked with scientists, clinical academics and technologists to produce translational research. I am currently investigating adverse health outcomes in people with common diseases using electronic health record data, and I also teach on the health informatics MSc joint programme with UoM and UCL.
I would like to see this platform encourage collaborations and the sharing of ideas and good practice across different disciplines that apply data science skills in their work (or as a hobby). I would like to support budding data scientists to gain useful advice and guidance for upskilling as well as application in real-world situations involving health data and biological data.
Hollie Johnson
I am a data scientist at the National Innovation Centre for Data (NICD), based in Newcastle upon Tyne. Following my undergraduate degree in mathematics, I worked as a software developer both in industry and as a technical research assistant in academia. I later joined the Centre for Doctoral Training in Cloud Computing for Big Data at Newcastle and obtained a PhD in topological data analysis in 2020. Now at the NICD, I specialise in transferring statistics and machine learning skills into industry, through collaborative data science projects.
I am excited to be a member of the editorial board and look forward to seeing Real World Data Science develop into a valuable source of information for aspiring data scientists and professionals alike. I would particularly encourage submissions that demonstrate the use of data science in SMEs and the non-profit sector, as well as perspectives from those with non-standard backgrounds.
Harvey Lewis
I am a senior technology leader, with a diverse background spanning rocket science, data science and research. I have 30 years of experience in artificial intelligence and other emerging technologies and am currently pioneering the use of AI in Ernst & Young’s tax and law practice. I’m a former member of the Open Data User Group, the Public Sector Transparency Board and the Advisory Committee to the All-Party Parliamentary Group on AI. I am a member of techUK’s leadership committee for data analytics and AI, and an honorary senior visiting fellow at The Bayes Business School in London.
I’m passionate about data science but I’m also a fierce advocate for human skills, which are as often underrated as AI is over-hyped. As a member of the editorial board, I’m keen to explore the interplay between artificial and human intelligence in businesses. I’m going to encourage all data scientists to think about the fundamentally human aspects of their work, such as trust and safety, so that we maintain perspective and proportionality in the face of ever-more sophisticated technology.
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.
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 Sasson
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.
Christopher Thiele
I’m a principal data scientist at Uniper where I lead a team that focuses mainly on financial business processes and upskilling initiatives. We run projects end to end: from use case ideation, requirement collection and translation to prototyping, assessment, deployment and maintenance. Besides the core analytical and data engineering duties, overarching topics such as data design, data governance and data strategy predominate my days. In my previous role, at the German Economic Institute, I contributed to the development of a cross-functional department that helps apply data science methods in economic research. Projects often involve geospatial analyses or natural language processing. Before that, I worked as a data scientist in customer and marketing analytics, doing statistical analyses such as marketing mix modelling. I have a master’s degree in statistics from Warwick University, a bachelor’s degree in economics from the University of Cologne and I’m trained as an assistant tax consultant in Germany.
I see data science as a creative way to solve problems using software engineering and quantitative modelling techniques and I like to build software pieces that people can interact with. I think that there still exists a lot of confusion about data science as a discipline. Reducing it would promote the realisation of its potential for individuals, as a profession, and our society, as a form of digitalisation. I hope that with Real World Data Science we can provide guidance and clarification to everybody engaged or interested in the field and accompany this young profession’s development.

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