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Lorna Brightmore, Lead Data Scientist, tails.com

What has been your career path to date?

I left academia after completing my PhD in Random Matrix Theory and spent the next year volunteering at FareShare in Bristol and travelling around South East Asia. Shortly after returning, I joined the Civil Service as an operational researcher. I was at home here – being part of a bigger organisation and working on technical problems to serve a sort of common good felt quite similar to working in academia and the problem space was interesting.

 

I started getting itchy feet when I learnt more about data science and the different tools and techniques other companies were using to solve similar data problems. At least at that time, I wasn’t able to bring these tools and techniques into my role. So I left the Civil Service to join the Faculty fellowship program in 2016 and get more practical experience working as a data scientist. As part of this fellowship, I did a project with tails.com and I’ve been there ever since.

 

While the core values and company culture at tails.com has remained unchanged, the company today looks different to the small start-up I originally joined. As the company has grown, the needs for data and data science and the scope of my role have changed with it.

What made you choose data as your career focus?

What I enjoyed most from my PhD and from mathematics in general was the nature of problem solving – breaking something down, looking at something from different angles and finding clarity and understanding. But what I felt I was missing from academia is the opportunity actually to have practical impact with my work. So I chose to work in data to keep this technical problem-solving part of my job, while being able to have real impact. Looking back, I know I made the right choice.

How aware were you of data as a career opportunity during your education or early work experience? Does this need to be heightened?

I wasn’t consciously aware of data as a career opportunity for quite a while. At university, the most obvious and visible career paths were into teaching or finance. Even when I was finishing my PhD, leaving traditional academia felt like a bit of a leap into the unknown.

 

There is a lot more awareness now of data as a career path which is great, but of course there’s more to be done. I’d like to see more focus on highlighting the range of careers and opportunities available in data. Also, more encouragement and support for people who are thinking of a career change, or who don’t come from a university background in the usual STEM subjects.

 

Data, and especially data science, has an air of mystery surrounding it and its often assumed you need a maths or physics PhD to be a data scientist. As well as being untrue, it narrows the diversity of people choosing a data career. Diverse teams bring a wider range of perspectives and experience, and are ultimately more effective.

 

What are your key areas of focus for data and analytics in 2022?

One pet project I’m really passionate about is tails.com data labs (https://tails.com/gb/data-labs/). This is an initiative I’m spearheading along with our UK head vet to use our data to further the understanding of pet health and longevity. We’ve amassed quite a rich, longitudinal data set since we launched and we want to start sharing some insights back with the veterinary community to help improve the lives of dogs and their owners. It’s exciting to be a part of something that could have a big positive impact,and that’s so aligned with our company ethos.

 

Internally, I’m focused on the longer-term strategy for data science in tails.com. This includes outlining the specific skills and roles we need in the data team to execute that plan.

 

Tell us about any ambitions you have in terms of becoming a data leader.

I want to make data and data science accessible to more people and to continue creating useful data products that benefit our team and our customers. Internally, this means being a catalyst that inspires everyone in tails.com to be more data-driven, whether that means partnering on more cross-functional projects, or just supporting and encouraging others to up-skill themselves. Externally, this means being a public face and sharing my thoughts and ideas, supporting others to pursue a career in data where I can.

 

What key skills or attributes do you consider will be essential your success in this role?

Active listening and really understanding other people’s points of view and challenges to find areas on which we can collaborate. Being a great communicator, both of an inspiring vision for uses of data science and for telling great stories with data. Finally, leading and supporting a team to be at their best and produce high quality work.

 

How did you develop – and continue to develop – your current skills or attributes?

I was recently part of the Circl coaching program, which has really helped me be a better coach, listener and leader in general, and as a bonus get to work with young people from under-represented backgrounds learning the same skills. I seek out feedback from others to help me see strengths I can build on and set development goals and always try to learn from my mistakes. I try to note down my thoughts and reasons for decisions, so if something doesn’t go to plan, I can reflect on it. Finally, I try to be open about my development goals so it’s easier to hold myself accountable.

How do you keep pace with or stay in touch with your peer group? Do you see it as important to have an active professional network?

I’m a regular attender and active member of the Python data group and other meet-ups and stay in touch with my network at Faculty. The thing for me that’s most important from a network is keeping up-to-date with developments and different applications of data science and how other companies are structuring their data functions. Being part of the same company for several years means it would be easy for me lose sight of what else is out there.

Lorna Brightmore
has been included in:
  • Future Leaders 2022 (EMEA)