In this blog series to commemorate National Mentoring Month, we are celebrating some of our incredible mentors in the Data Science for All program.
In 2016, I was doing a PhD in Computer Science at University College London working with social media datasets and developing computational models to forecast financial markets dynamics. One day I was offered the opportunity to join a start-up in the Silicon Valley which worked in the AI / Knowledge Economy space as the first Product Manager (and employee) for what would then become the company’s Financial Services vertical. My challenge was to create the business from scratch and define not only the product roadmap but also create a business plan jointly with the CEO. During this period, I was lucky enough to have exposure to so many parts of the business, from forming and leading a team to dealing with investors, closing alliances with partners to designing, developing and selling products to some of the largest financial institutions globally. Although I had worked in large institutions before, this start-up experience was pivotal in my career as it has uniquely equipped me with invaluable business, technology and people lessons.
From my parents, I learned the importance of work ethic while my academic supervisors taught me the value of clarity. From my professional mentors, I learned how to lead without authority and from my colleagues, the value of diversity.
Mentoring has been a humbling experience. It is an opportunity to interact with a diverse group of people with fresh perspectives and ideas, which has always made me reflect on my own goals and improve awareness of my own learning gaps. I always leave mentoring sessions with refreshed curiosity and satisfaction from seeing others progress, particularly those from underrepresented groups such as the case for the DS4A / Empowerment program.
In our day-to-day work we deal with vast sets of data, from tens of thousands of diverse sources. Everything we do is guided by the scientific method and investment in people, technology and data is what makes our scientific approach possible.