Women in Data: Interview with Director of Data Science & Engineering at KPMG UK

What’s the most memorable moment in your career to date?

It’s difficult to answer that one. The most memorable moment for me was when our Chief Digital Officer, Lisa Heneghan rang me to say that I had passed my Directorship panel. Lisa was away in a different time zone and had taken the effort and kindness to call me with the news in her early hours. It’s also memorable not because it represents a title, but a journey.

I am from a low socio-economic, single-parent background and times were tough growing up – I had a mattress on the floor, we had no carpets, garden furniture doubled up as lounge furniture and, we would need to put pound coins in the TV to make it work.  I was fortunate that that State looked after me and I was gifted an Assisted Place to attend boarding School – so education has, and continues to be, a real place of sanctuary for me.

I was absolutely determined to make a life for myself and also give back to society; I saw education as the vehicle for those outcomes. I have been through the care system, had periods of homelessness, the first person to attend University in my family, and now people see ‘Director’ at KPMG with a PhD; they do not know what it’s taken to get there and that’s why it’s memorable.

What kind of improvement, change or growth are you trying to achieve at KPMG?

Professionally speaking, to continue to lead and grow our data science delivery capability to build technology solutions/products with our clients that improve our public services.  But the nature of how we deliver those advanced technologies for our clients is what matters most to me – I run and encourage a flat structure in my team. It’s not your title that matters – it’s the technology skills you bring to the build.

Each Sunday evening, I take a few hours to reflect on the week’s scenarios and really challenge myself – ‘could I have handled that in a different way?’; ‘how would I feel in that situation?’; ‘what are the challenges my team member(s) have, given the pandemic and have I offered all the support I am able to?’. I think leadership is a lifelong learning journey – so growing as a leader is very important to me and encouraging an open and safe environment, where people feel supported and nurtured, is what I am always looking to achieve.

As you can probably tell, I am a passionate advocate for diversity and inclusion, and I remember early in my KPMG career a female Partner saying to me ‘KPMG is a platform – use it drive positive change’. I often reflect on that advice and KPMG is hugely active in moving the IDSE dial. It has given me the opportunity, as a neuro-diverse, gay woman to bring my whole self to work. We have just passed our 150^th year and have vibrant communities committed to driving equality of which I am an active member and in turn, I am growing a huge amount (e.g., ‘ITs Her Future’ – women in technology; ‘Breathe’ – LGBT+; ‘Black Lives Allyship’ and ‘Workability’ – disability network).

I am very passionate, given my past, on growing our Corporate Social Responsibility agenda and as part of that I was supported to volunteer 1 day a week at Great Ormond Street Hospital throughout 2020 as their Data Science Strategy Lead – the importance of data and public-private partnerships has never been so needed in light of the pandemic. My data science team at KPMG are currently working with our National Charity – the NSPCC – to use data science techniques to understand the toll of Covid-19 on Children’s mental health.

What do you think are the biggest challenges facing data scientists right now?

I think the biggest challenge is showing what it means to ‘prove value’. I see a lot of organisations hire a data scientist and think “We’ll leave them in the corner and they’ll just do something that will drive business change and value.” Whereas actually, it really doesn’t work like that. It really is a clash of worlds to ensure that you as a data scientist are having a fulfilling and purposeful career in an organisation, but also that the organisation understands how to give that to you.

I am not Hermione Granger, you don’t just give me data, I wave my magic wand and ‘ta dah’ stuff happens. That’s a real challenge for data scientists that I see in industry, they go into industry or a business and there’s a high churn over rate and perceived failure, which is crushing for all involved, but actually it’s because organisations don’t know how to communicate or work with data scientists – it’s about the alignment of the right problems to solve with the skillset that data scientists can offer in a delivery team – which is my role to provide.

Male analysts and data scientists outnumber females 4 to 1. What advice would you give to businesses struggling to attract and retain senior female talent?

Well, I think it’s not necessarily just the senior, it’s across all ranks we are seeing this problem. Thinking about senior talent requires thinking about the entire talent journey from encouraging girls into technology and them seeing that as a non-gender related career choice, through to employers designing adverts that appeal to all people in society. We are very aware of this at KPMG and have taken real steps to look at the language we use in our job descriptions and seen our percentage of females applying for technology roles, increase. Words matter – rather than ‘manage a team’ in your job spec, how about using ‘develop and nurture a team’?

I think there is a real sense of looking up and not seeing female role models – which has sadly been amplified in the pandemic, with Forbes reporting that significantly more women in tech being laid off or furloughed and typically taking on more caring responsibilities.  Anyone who would like a chat – I am on LinkedIn and would love to hear from you!

I would also encourage employers to be far more open to people’s background. You do not have to be a chess champion or physicist to be a data scientist – I’m not. For example, one of the best programmers I know is a historian, I doubt they would even get through a (human/AI) CV shift now. When you’re recruiting, I would really encourage concentrating on people’s aptitude to learn and problem-solve, not what they have learned.

Finally, and for me this is a deal-breaker: What is your Employee Value Proposition? Most jobs (if we are lucky to have one currently) offer similar financial reimbursement etc. I want to join an organisation that cares about its communities – that actively encourages and supports me to volunteer my data science skill set, like I do at Great Ormond Street Hospital. I speak to many technologists that happen to be female, where this is really important to them. Organisations are very much being put under increasing scrutiny on this point and I think that’s a real positive.

“Data science has an image problem” has been said a lot in the news recently. What would be your advice to employers wanting to change this perception and create a more inclusive culture in their business?

I think we do an injustice to data science because we need to talk about data science in the context of purpose. We talk about it in a very esoteric way, talking about nonlinear algebra and calculus and all the things that yes you need to know and are super important, but actually, what is the outcome of doing those things?

The outcome could be building a chatbot for Great Ormond Street Hospital that enables frontline staff to potentially understand more about their patient by being able to ‘talk’ to the data. But we often divorce purpose from data science, and I think we need to bring that back into the conversation; front and centre because who doesn’t want to positively change the world?

What excites you most about recent developments and future of data science and AI?

What excites me most, which sounds like a bit of a paradox, is that I’m seeing far more organisations think about ethics and what that means from a workforce perspective. We use data science products every single day like Google, Netflix and Siri. Whatever it is, algorithms are intertwined into the fabric of our society, but I don’t think there’s been enough focus on regulation.

We shouldn’t shy away from regulation; I don’t think it stifles innovation at all. I still find it remarkable that I could potentially lead an algorithmic build that could really impact society, but of course I am not allowed to do an audit because I’m not trained, qualified or a member of a professional body to do an audit. There doesn’t seem to be that equivalence in what we do.

We talk a lot about these algorithms, but it’s no one’s ‘job’ to monitor if they perform how they should or how we predicted them to. So, for me it’s really exciting that there is this focus on algorithmic bias and ethics; really holding organisations to account. We wouldn’t allow someone to let off a nuclear bomb and yet, in technology it seems to be very different view.