Kylie Norman is Co-ordinator of the Data Scientist Development Programme at the University of Leeds. The programme offers early career data scientists the opportunity to hone their expertise working on real-world data and topical research questions, whilst giving academics and Leeds Institute for Data Analytics’ partners the chance to harness new talent in finding novel ways to solve data problems.
Tell us more about your role at the University of Leeds.
I lead the Data Scientist Development Programme (DSDP) at Leeds Institute for Data Analytics (LIDA). The Programme is currently in its eighth year, and I’ve had the privilege of being one of its leadership team for seven years. In that time, I’ve developed skills in line managing early-career data scientists, fostering key stakeholder partnerships, securing innovative data science projects and building the DSDP’s network and reach across the University of Leeds and beyond. With the DSDP leadership team, I employ early-career data scientists to work on 6-month data science projects in partnership with academics, industry and public sector stakeholders in order to create data science solutions to challenges affecting health, environmental and societal goods. The particular specialisms I’ve developed through management of the DSDP are advocacy for equity, diversity and inclusion (EDI) and coaching for professional development and well-being. Currently, data show us that the greatest challenges to continuation in the UK data workforce are the rise in mental ill health and a lack of support for early-career data scientists. I use my EDI and coaching skills both to increase the diversity of our data scientist cohorts and to focus our data scientists’ training as much on professional development as technical, thereby enabling data scientists to thrive and continue on into the data workforce.
What do you enjoy most about working in your sector?
I enjoy working in a data institute that values empowering its staff and giving them the trust and space to respond to challenges and trial new ways of doing things. This is partly the benefit of working in a University setting, where learning and curiosity are normative. LIDA’s fostering of interdisciplinary collaboration means that I get to work with many different minds and perspectives in a collegial way, and this has created an environment in which the DSDP’s partnership model of working has naturally developed and flourished and in which it’s been possible to build and maintain cross-cutting, cross-sector relationships between academic and industry colleagues. Innovation doesn’t happen in a vacuum, and partnership is at the heart of everything we do on the DSDP, from consensus leadership, to a multi-disciplinary advisory group, co-produced values and research culture in collaboration with our data scientists, and projects co-designed by academics, public and private sector stakeholders. One of the results of this partnership model is that many of our data scientists go on to work for our external partners and we’ve seen them develop into data science leadership positions at national organisations like Office for National Statistics and Morrisons. Partnership builds trust, and the DSDP is a trusted pipeline for skilled data scientists.
How important is innovation within your role?
Innovation, to me, means finding novel ways of doing things which enable wider conversations taking place across a sector to move forward. Innovation in data science and AI is important because these fields are rapidly evolving, with new applications for methods and techniques emerging all the time, and increasing opportunity for cross-disciplinary solutions. In considering innovation in the context of my role, I realise that I’ve typically associated it with what other people do and I wouldn’t necessarily have referred to myself as an innovator, even though one of my core values is curiosity. It’s useful to realise this limitation in my self-perception. When I think of innovation as novel ways of doing things that move a conversation forward, I can see that I’ve been innovative in my approaches to diversifying data science and the development of early-career data scientists. I was the first at our University to successfully implement positive action recruitment of black people and this has led to becoming a known EDI ambassador and moving conversations on recruitment best practice forward. Across the sector, we embrace innovation and we recognise the need to measure and ethically-assure it. Similarly, when we introduce new approaches to recruitment, training and employability-building on the DSDP, we ensure that we measure their impact, and work with our advisory group to ground-truth our rationale for making changes. In this way, innovation is encouraged, sense-checked and measured for its effectiveness, and the findings in turn shape our strategy going forward.
What professional achievement are you most proud of?
This year I’ve led on establishing a formal DSDP mentoring service, using my qualification as a CMI professional coach and tailoring our service to meet the needs of our early-career data scientists. This is a strategic response to an industry-wide need for early-career resilience and support. We can speculate as to why this need is especially prevalent in data science; my experience has shown that it’s in large part owing to the shifting and fast-paced evolution of data science and AI and the vastness of the skills base for digital and data literacy, factors at once exciting and unsettling due to the higher uptick in managing expectations in a changing landscape. Previously having worked with colleagues to instigate a DSDP ‘buddying’ scheme under the COVID-19 pandemic, I’ve now developed this into a mentoring framework of dedicated mentors, structured meetings, a resource library, well-being support and peer mentor supervision in order to empower early-career data scientists to own their strengths, identify key areas for growth and develop self-awareness. One of my objectives with this service is to enable every data scientist to uncover strengths that could take them into future leadership roles. In this way, I and my fellow mentors are not only building resilience and employability, but also accelerating trajectories for future leaders across a diversity of backgrounds. In our most recent DSDP pulse survey, LIDA data scientist respondents all agreed that mentoring on the DSDP contributes both to their sense of belonging and well-being.
What, if any, are the challenges of being a woman working in your sector?
Women are underrepresented in data science and AI professions both in the UK and internationally, and career progression into senior leadership roles remains challenging and often relies on sponsorship in the form of advocacy from colleague-allies. In spite of making up just over half the UK population according to the 2021 Census, women represent only 22% of AI and data professionals.[1] According to the World Economic Forum’s 2018 Global Gender Gap Report, international recruitment of women into data science roles is even lower at 15-22%. The resulting challenges for data science – as for all sectors with known underrepresentations – include loss of talent through lack of access and the risk of unconscious bias from recruitment to data science methodologies and solutions. My commitment to increasing equity, diversity and inclusion (EDI) in data science has led me to work with key partners to disrupt known underrepresentations in our sector. We use positive action recruitment (of women, among other protected characteristics) in order to increase the diversity of our data scientist cohorts. This involves active messaging to raise awareness and engage the target audience and proactively reserving data scientist posts for women of equal merit. As a result, we’ve successfully increased the number of applications to our Programme from women, and seen the number of women appointed to the DSDP rise to 55% in the last 2 years.
What advice would you give to other women working in your sector?
- Work on your understanding of how you operate, your values, strengths, ways of working and what motivates you. When you understand these things, they become a shorthand for key decision-making and will direct your career choices; you’ll also better understand and trust your gut instincts in situations.
- Identify allies who are working towards similar goals and who bring different expertise and perspectives to bear on the challenge. By being open and curious about other perspectives on the challenge at hand, you’ll find that you automatically attract allies and collaborators.
- If you have the opportunity for coaching or mentoring, either as a giver or a receiver, take it. Mentoring is a safe space to question, explore, develop self-knowledge and engage with different perspectives and levels of experience. By being a mentor yourself, you have the opportunity to use your experience, advocacy and allyship to help junior colleagues develop into roles in a resilient and often accelerated way.
- You can’t do everything. The more successful you are at your job, in particular if you’re someone whose name is associated with change-making, the more opportunities you’ll be offered and the harder it can be to say ‘no’. Refer back to point 1. When you understand yourself and the landscape in which you work, your decision-making automatically becomes strategic. Personally, I have found that I’m most satisfied in my work when I’m working in a congruent way with my values and my overarching work purpose.
How does what you do fit into the wider narrative of the Digital Skills Gap in the UK?
Like others in our sector, we’re responding to two key challenges around capacity currently. The first is that of demand outstripping supply: reportedly, the current turnover of UK graduates can only fill 13% of the annual ca. 78,000-strong data scientist jobs in the UK. The second is the Digital Skills Gap – the gulf between digital literacy in some parts of the UK compared with others. This skills gap is most apparent in areas of high deprivation, and is often intersectional with complex socio-economic, environmental, health (particularly mental ill health) and demographic factors. It requires a multi-stakeholder, multi-level approach to tackle it. The DSDP is a scalable model which has the potential to provide a solution to the first challenge because of our combined approach to technical and interpersonal skills training in early-career data scientist development. Through positive action recruitment, we’re also increasing access to the data workforce for those who previously haven’t considered data science jobs to be for them. In response to the second challenge of the Digital Skills Gap, it’s critical to locate interventions at the school stage in partnership with communities in the greatest need. Last year I led our data scientists in the delivery of digital skills workshops for local pre-GCSE students in the Keighley Schools Together partnership. LIDA data scientists designed digital engagement activities which responded to place-based needs and challenge factors, from teaching Scratch coding to analysis of nutritious diet data, in order to increase relatability and appetite for data science careers. LIDA Open Data Science for Schools is now part of core DSDP activity.
[1] The Alan Turing Institute report, ‘Women in Data Science and AI’. https://www.turing.ac.uk/research/research-programmes/public-policy/public-policy-themes/women-data-science-and-ai
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