In this blog series to commemorate National Mentoring Month, we are celebrating some of our incredible mentors in the Data Science for All program.
Jolyon Bloomfield is Senior Data Scientist at Point72, and a mentor for DS4A / Empowerment. He answered a few questions for us about his experience, career advice and creating more inclusive workplaces.
Tell us about a pivotal moment in your career: what was the backdrop, what did you do, what did you learn?
After completing my PhD in theoretical physics, I spent 6 years researching and teaching physics at MIT. Then my wife was sent to the suburbs of NYC to continue her medical training, and I was unable to find an academic job in the area. Despite my intentions of being an academic for life, I had to leave my beloved physics and find a “real job”. I chose to pivot to data science, as the skillset has a fair amount of overlap with physics. However, I knew that to excel in this field, I’d need a job where I could really get my hands dirty with data and the technologies used to handle it. That’s part of the reason I chose a job at a hedge fund where I work with “alternative data” to extract trading insights. During my time at Point72, I’ve learned about cloud computing and working with massive data sets and have also developed insights into the mental paradigms needed to effectively evaluate, process and draw understanding from data.
Who were your mentors and role models when you were starting out? What’s the best professional advice you received?
I honestly didn’t have many mentors or role models, particularly in the data science field. I made a sudden and abrupt switch to data science, and my physics network didn’t really have anybody who knew what I could expect. Academia can be quite sheltered in that way! Somebody I really look up to though is Doug Finkbeiner, who is a Professor of Astrophysics at Harvard University. Every time we catch up, he’s playing with some new aspect of data science and trying to apply it to astrophysical data. Keeping up with the details behind the various things he tries keeps me learning and thinking of weird and wonderful ways to apply strange techniques in previously unthought-of ways.
In terms of advice, an old physics professor once drilled into me that it’s not enough to have an answer – you also need to understand your answer. I see this constantly in my recruiting nowadays, where the people who really stand out are those who not only excel at a given task, but also seek to understand and evaluate their results in the context of the problem.
What have you gained from mentoring and coaching - both professionally and personally? Did anything surprise you when you started mentoring?
Towards the end of my academic years, I recognized that my greatest impact on the world was not going to come from my research, but from teaching and mentoring students. Particularly for the students who I mentored over a span of years, I felt that I’d helped shape their future for many years to come. I found this aspect of mentoring to be quite rewarding. I’m not sure that there were really any surprises when I started out – the surprises came from being in it for the long haul!
Can you give us an example of how data skills are increasingly needed in your role and business?
I work with what’s known as alternative data in the financial industry. These are non-standard datasets from all sorts of weird and wonderful places that we use to gain insight into the performance of companies. The amount of data that we process is steadily increasing, and we need people who are capable of diving into and understanding vast troves of unstructured data with the goal of extracting useful information. As I mentioned earlier, the people who excel at this are the ones who strive to develop understanding from what they see, rather than simply taking the straightforward result as their answer. This deeper understanding often arises from pulling at the threads of odd behavior and teasing out the subtle correlations that indicate what’s going on at a more fundamental level.
You’ll be mentoring a group of Fellows in our inaugural cohort in the Data Science for All/ Empowerment program, an initiative to create equal opportunities to access the data-driven jobs of tomorrow. What can individuals and organizations do to help create more diverse and inclusive workplaces?
This is a complex issue, and we’re actively having internal discussions on exactly this question. At the end of the day, I think that individuals and organizations have to realize that there aren’t going to be any quick fixes here, and that it’s imperative that they commit to long-term change by continually monitoring and adapting their processes. We’re constantly looking for ways to interest a broader group of people in what we do, improve diversity in our recruiting pipeline, and create an inclusive community. I think it’s also important for everybody to partake of some serious self-reflection and self-education – acknowledging these issues is an important first step, but we need to take the time to understand where we are and how we got here in order to make decisions that will lead to lasting change.