What attracted you to a career in data science?
I can answer this question from two perspectives. From a practical perspective, data science fits my qualities and interests. I’m very detail oriented, I like to get to the root of problems, and I enjoy fitting complex problems into an easy to tackle, analytical framework. I love coding, and a beautiful data visualisation can make me (embarrassingly) happy.
However, from a more philosophical perspective, I’ve always said that data science is like mind reading. We take incomplete data on human, erratic behaviour, and use that to try and understand the thought process that created that behaviour. That is just fascinating to me! Data science is so much more than coding and modelling: it’s understanding behaviour, psychology, social interactions… That’s what really captivates me.
What’s been the most memorable moment in your career to date?
That must have been when I went from an individual contributor to managing other data scientists. I really enjoy biting my teeth into a complex data science problem, and there have been many projects during my career that I am really proud of. But there’s something incredibly rewarding about seeing other data scientists do amazing things and help them reach their full potential.
I’ve learned so much from working with data scientists from different backgrounds and seeing how they approach problems and work through them. I’m hoping to do much more of that in my current role.
Just 5% of the leadership positions in tech are held by women. Do you think enough is being done to create more gender balanced leadership teams?
I don’t think we are doing enough to combat the source of the problem: our outdated perceptions of what a good leader looks like. Having more women in leadership positions is not about gender, it’s about diversity in thought and leadership style. We need to start recognising that leaders can be quiet, compassionate, and thoughtful, and realising how different perspectives can help everyone move forward. That way it becomes an issue that is not just about gender, but about any minority group that has been overlooked for having a different work and leadership style.
What advice do you have for female data professionals striving for leadership positions?
The single best advice I can give to women striving for leadership positions is to believe in yourself. You don’t need to be the best at everything or know everything about data science to be a great leader in this field. Data science is an extremely wide field with so many specialisations, and I’m the first one to admit that I’m not an expert in many of them. Focus on building a great team of diverse talent around you and learn from each other.
What excites you most about recent developments and the future of data science?
I’m really excited about the fact that data science is starting to penetrate into so many industries. The field is becoming wider and wider, with people from different backgrounds contributing to it. I like this cross pollination of ideas and expertise. However, it’s hard to keep up with so many new methods and techniques being developed. That’s why people are starting to specialise in a specific data science technique or industry, to give them that edge. Bringing data scientists with different specialisations together, that’s how we can really create innovation.