What I learned about Career paths after spending a decade in Data Science
Not everyone wants to be a leader. And not everyone wants to code for the rest of their lives. And then there are some of us, who want to do both. For as long as possible. Having spent around 10 years in Data Science now, there is some wisdom I want to share on growing in your data science career. It’s already been 10 years since Harvard called Data Science the sexiest profession of the 21st century. In spite of this “sex appeal”, I know for people like me and my friends in the industry, the balance between the technical path and the management calling can often be extremely unsettling.
Technical route or Management route?
Does growth tank when we decide to take the technical route? Isn’t management far more lucrative? After all, Sundar Pichai didn’t become the CEO of Alphabet by remaining hands-on and technical!
And then there’s another part of our brains that knows that Mr. Pichai probably spends an unhealthily long number of hours chatting with lawyers and lawmakers. Not a fun job if you’re a nerd! So is his management style job as lucrative after all, and is that something you should aspire to become?
When I was at a decision point a couple of years back, I focussed on my strengths
When I was faced with this challenge, I thought long and hard about my strengths, and what makes me unique as a data scientist. This is something I’d recommend every data scientist does when they’re starting their careers and then keep stock as they progress. I also wondered what my 50-year old self would want out of her life and career. When you think through things in decades, a lot of life comes into perspective and this helps you focus on your life goals, rather than focus on your 2-3 year career goals.
My career path
Let me tell you a bit more about my last decade. I got a Masters in Economics from Indira Gandhi Institute of Development Research. It was set up by the Indian Reserve Bank (equivalent to the Fed in India) for Economists. My thesis guide was one of the brains behind the Indian Nifty index (equivalent to the DOW Jones in India). During those days, R was the language for the cool kids. And my Professor made sure that I was working with real-life financial data. So when I started working, I had the advantage of:
Experience in a language which was relatively new to the industry
Experience manipulating real-life messy data
A degree that blended the quant with business - and made me more strategic than the next quant
A flair for academic writing and a master of interpreting graphs and charts
And because of my personal interest in financial markets, an understanding of credit and market risk, far greater than most experienced folks in the market in the aftermath of 2008
So I decided that I wanted to be a Consultant to Banks whose main job was crunching data and making sense of the numbers. I joined Deloitte. I believe I did fairly well, though one of the things I didn’t get enough of there was mentorship from people who had followed a similar path.
3.5 years later I moved to London in the UK. I realised quickly that my skills were in demand because people were still learning R and figuring out how to measure the risk on their books post the Global Financial Crisis. But by this time, I was already a bit tired of just looking at risk. I wanted to be a part of the growth stories. So, I joined the AI team that was in its budding stage at PwC, Strategy&. Here I ended up finding a lot of mentors and people who I could truly say that I wanted to be like when I was their age. This team changed the course of my career. And it wasn’t just this team - it was the firm as a whole. I got amazing mentors- ones in data science and others not so much. PwC made me reflect truly on what my strengths were and it’s probably fair to say that I figured out quite quickly that I wasn’t as smart with my code as Google would expect from their engineers. Could I be that person if it tried? Sure! Did I enjoy writing code? Of course!! Did I see myself growing in the technical field writing code? Nope!
So what did I have that most others didn’t? I again sat down and listed my strengths:
I knew the theory behind most of the algorithms and understood them well enough to teach the non-technical clients we had
I had mastered the art of PowerPoint storytelling and could speak data in normal English
I loved to strategize and was good at it
I loved to talk to people about themselves and their business problems
I loved finding data-led solutions to business problems
Given this, I realised that the pure technical route wasn’t for me. I needed the strategic part of my brain to be solving business problems, the quant part of my brain to be learning the theory behind ML that most data scientists ignore, and the creative part of my brain to be teaching this to businesses as if they were five-year-olds. I had 3 options from there:
Start my own firm - I couldn’t afford this at the time
Join a pure-play data science consulting firm - Being location-sensitive, I found only 3 players who matched what I wanted to do in London. All of them were too new and I was worried about my mortgage.
Set up and build a data science team in industry - This is what I ended up doing, and am currently at this job heading a data science team.
While I am still as technical as I want to be, I have both the autonomy and freedom to scale the technical or management piece up or down as per my interest. I have realised that business management and becoming the CEO isn’t my end goal. In fact, I don’t even want to be CTO. I enjoy the nerdy side of data science, I love solving business problems and I love developing people. Does my 50-year old self agree? Absolutely! She’s already retired, living in the mountains and possibly teaching data scientists how to make themselves boardroom-ready. But your 50-year-old self will look much different from mine. In fact, in a couple of years, my 50-year-old self will look different from my current 50-year-old self. You see, that’s the beauty of it all - nothing we do with our careers is ever set in stone. We can always take U-turns and leverage skills we would have never imagined using. But what I’ve found is, to leverage, you need paramount amounts of self-awareness.
A questionnaire to help you focus on your strengths through this journey
So here’s a questionnaire that might help you think through some of this. While answering these, remember to count the number of years between the current you and you at 50. And also remember, we end up overestimating how much we can do in a year and underestimating how much we can do in a decade.
What is my core Data Science skill set?