Natasha Kelly once thought that she would eventually become a professor. She quickly realised that her passions lay elsewhere, which led her to a life sciences analytics role at Accenture.
Career changes are very normal. This generation of workers shouldn’t expect that they will remain in one sector for their entire lives – one of the many ways in which this world of work differs massively from the one that preceded it.
Natasha Kelly had for a long time aspired to the coveted tenure track within academia. She did an undergraduate and PhD in zoology, evolutionary theory and biology in particular. She worked as a biological researcher for a while, mainly writing mathematical models.
Yet even though this was the career she had envisioned for herself, it ultimately wasn’t making her happy. She had a “laundry list” of things she wanted from her career and found that management consultancy, rather than academia, ticked more boxes.
Here, Kelly explains how she got into her role at Accenture Digital’s applied intelligence team and about the people that have inspired her on her career journey.
What first stirred your interest in a career in data science?
I don’t want to say that I fell into data science, because I made some very deliberate steps towards it once I knew it existed as a career. For most of my early career, I thought I would end up working as a professor of biology.
Once it became clear that I wasn’t suited to a career in academia, I developed a checklist of what I was looking for in a career. I wanted to work with people in a problem-solving capacity, I wanted to be able to use my mathematical and analytical skills, and I wanted to have the opportunity to pass on knowledge to others, be it through teaching, coaching or mentoring.
What education and/or other jobs led you to the role you now have?
My path to this career is a bit meandering. I did my undergraduate and PhD in zoology, specifically evolutionary theory and behaviour. Then I worked as a biological researcher in academia – mainly writing mathematical models to optimise resource distribution and predict behavioural responses.
When I decided to leave academia, the idea of data science as a career path was relatively new. I joined Accenture in 2013 and started out doing fraud and risk analytics for banking and insurance clients. I then moved into supply chain analytics for a client in technology manufacturing.
What were the biggest surprises or challenges you encountered on your career path and how did you deal with them?
The biggest challenge to my career path was actually myself. That sounds trite but, like I said, when I first started my career I thought I was going to be an academic researcher and a professor. I had it all planned out: Ivy League PhD, prestigious research roles and tenure.
I realised as I got further into academia that I was increasingly unhappy in my career. I was stressed out and dreading going into work, and it forced me to take a step back and ask myself why was I that unhappy.
Was there any one person who was particularly influential as your career developed?
My PhD adviser Suzanne Alonzo, who is now a professor at UC Santa Cruz, has always been passionate about gender parity in the workplace. She used to host lunchtime discussions about recent studies and research that had come out exploring why the percentage of women dropped as roles became more senior, why women apply for some jobs and not others, structural barriers to gender equality etc.
The advice that she gave me has stuck with me and driven me ever since, and it was simple: “Be honest with yourself about your own abilities. Never think that you are ‘not qualified enough’ for the job.” Her point was that women tend to underestimate their own abilities and play down their qualifications. If you think that you can do a job, then you are qualified to do that job. Don’t second-guess yourself.
What do you enjoy about your job?
The variety. I’m always learning. Every single client is different, even when they’re in the same industry sector. The manufacturing industry in general – and life sciences in particular – is undergoing a massive transformation in terms of the amount of data they generate, what they can collect, how they store it and what they can use it to do.
The industrial internet of things, edge computing, and advances in analytical techniques like computer vision, natural language processing (NLP) and deep learning has fundamentally changed the landscape for how and why life sciences companies use analytics.
What aspects of your personality do you feel make you suited to this job?
I’m a bit of a contradiction personality-wise, which seems to work for this job. I can be very analytical, very obsessive about finding a solution, and I’ve a very good memory and ability to look at problems from different angles. That all serves me well as a data scientist. At the same time, I’m also pretty chatty, really enjoy storytelling and working with people to understand any issues they’re facing. This is great for the consultant side of my role and the mentoring/coaching side as well.
How did Accenture support you on your career path, if at all?
This is probably not the most informed answer because Accenture is the only company I’ve worked for as a data scientist. However, the support that I’ve received here is a large part of why it’s the only company I’ve worked for.
Accenture has supported me at every step of my career as a data scientist. For example, management listened when I told them I was unhappy working in a particular industry or when I felt like my role was starting to get repetitive, and facilitated moves into new areas that challenge me.
I think the biggest thing that Accenture has done to support me is to give me the opportunities to drive initiatives that I’m passionate about.
What advice would you give to those considering a career in this area, or just starting out in one?
Take every opportunity you can to learn new skills – whether that’s online courses, hackathons or formal education. Data science is growing and expanding as a career path, and the skills needed are becoming broader. The type of analytical techniques which we would never consider five years ago due to their complexity are now becoming commonplace, as processing power increases.
Having said all that, I’ll go back to Suzanne’s advice to me. “Be honest with yourself about your own abilities. Never think that you are ‘not qualified enough’ for the job.” Oh, and learn Python.
