What is a data scientist’s career path?

Data Scientist Career Path :)

Answer by William Chen:

My friends and I interviewed Jonathan Goldman, who is currently Director of Data Science and Analytics at Intuit. He's a rare example of an extremely experienced data scientist with a relatively long career history in data science! Check out his LinkedIn at Jonathan Goldman.

I wanted to share some parts of his interview to share what a data science career progression looks like for one of the earliest data scientists in the industry. Specifically, Jonathan's career progressed in the following order:

  1. Bachelors in Physics at MIT
  2. PhD in Physics from Stanford
  3. Business Analyst at Accenture
  4. Data Scientist at LinkedIn, becoming lead of Product Analytics team
  5. Co-founded Level Up Analytics, a data science consulting firm
  6. Startup acquired by Intuit, where he became Director of Data Science and Analytics

A bit about Jonathan:

Jonathan is currently Director of Data Science and Analytics at Intuit. He co-founded Level Up Analytics, a premier data science consulting company focused on data science, big data and analytics which Intuit acquired in 2013. From 2006–09 he led the product analytics team at LinkedIn which was responsible for creating new data-driven products. While at LinkedIn he invented the “People You May Know” product and algorithm which was directly responsible for getting millions of users connected and more engaged with LinkedIn.

He received a Ph.D. in physics in 2005 from Stanford where he worked on quantum computing and a B.S. in physics from MIT.

On Jonathan's Transition from Bachelors to PhD to Consultant at Accenture

Jonathan finished his Bachelors in Physics from MIT. He wasn't quite sure what to do with his future afterwards, so he applied to PhD programs. Slowly, he realized that the academia life was not for him. He joined Accenture as an analyst, as he was interested in energy and didn't want to do a postdoc.

I completed my Bachelors in Physics at MIT. I just absolutely love math and physics. I actually loved a lot of other fields as well, but knew I wanted to stay with math and physics in particular. I also absolutely loved MIT — it was the perfect place for me. When it came to graduation, however, I still didn’t know what I wanted to do with my future. I knew I wanted to do something more in science, but I didn't know if I definitely wanted to be a professor. I ended up applying to Ph.D. programs but still wasn’t certain if that was what I wanted to do.

I also applied for a few jobs, but was just not excited about any of the jobs I saw, and how they would leverage my skills. In comparison, grad school was exciting since I would get to work on fundamental research there. At the time, I was really excited about what was happening in the world of quantum computing.

I got into Stanford, and I found an advisor who was specifically working on quantum computing. So I came out to Stanford and liked it for a while, but towards the later part of my Ph.D. recognized I wanted to something else. Research was hard and not as rewarding in the short-term — it took me seven years to get the results that I needed to graduate. It was in my fifth or sixth year that I thought, "I want to do something that has a little bit more immediate impact.”

The parts of the Ph.D. program I loved most were when I was actually getting the data, analyzing it, and iterating very fast. I had these experiments I'd have to run for 30 hours, and basically after that, the system would shut down, restart my experiment, and it would take a day or two to get the system to reset. It was during this period that I was getting this amazing data, make a hypothesis then and test it. I loved the actual thinking, the theoretical aspects of it, what that told me to do with the experiment, and what parameters to explore.

Towards the end of my program, I got involved in some entrepreneurship activities at Stanford. I got involved in this organization called a nanotechnology forum, where Steve Chu, Stanford physics professor and later the Secretary of Energy came to speak. A lot was happening back in the early 2000s in that area. I was trying to go into that area, looking at solar energy technologies — I was very excited about that. But then I looked at a few of the solar technology companies, and the basic approach that they had was, “Hey, you get to work on this technology as a postdoc, and if it works, you’ll get a full-time job. If not, that’s a nice postdoc for a year or two.”
That just didn’t seem appealing to me.

At the end of graduate school, I was looking for a job, and I knew at that point I just did not want to stay and do a postdoc. I ended up going to the consulting firm Accenture, and I was excited about going to work in energy. I had been working on energy-related stuff, and I was getting more excited and interested in that. I wanted to work in strategy for Accenture — the focus was in the utility/energy sector, especially in the natural gas market.

So, I was working for a little while on natural gas strategy for one of the partners, and that was fun. I got put on a project to work at a utility company, and it was good to get that exposure — to find out what the corporate world is like. What is it like to do consulting? What’s it like to work in this company? How do they operate? I actually learned a lot about how to communicate and how they work; it’s such a different world from academia.

On Jonathan's Transition from Accenture to LinkedIn

Jonathan left Accenture looking for a more technical role, and was blown away by the people and the data at LinkedIn.

At that point where I was thinking, “Let me see if I can do something a little more technical.” I felt like I learned what I needed to learn so I was trying to find new projects. I started looking around to new places, including LinkedIn. Initially it seemed like it was a recruiting platform and I wasn’t that excited about it, but after I went and met with various people there, learned about their data, and learned about what they were thinking about, I thought "Wow, this is awesome."

Why Jonathan Found LinkedIn Awesome

Jonathan transitioned to being one of the first data scientists at LinkedIn, and was one of the original creators of LinkedIn's now famous People You May Know algorithm. Jonathan is profiled in Data Scientist: The Sexiest Job of the 21st Century, one of the foundational articles of data science.

Well, what really excited me was thinking, “Well, look, you have this data about people's careers, where they went to school, where they are now working, what they have done in their careers, and descriptions of their past jobs. So how do I help people get the right job?” It's a problem that actually felt very personal. While I'm trying to find the right career for me, I could help work on solving that problem for others at scale. 

The data was all there, and I could ask questions about the data very quickly. It was exactly the part of the Ph.D. program that I liked. Suddenly I didn't have to deal with the experimental apparatus which took me two years to build. It was like, boom, I have the data, and it's actually very interesting. I was learning all these new techniques and it was great.

Within two weeks of starting, I had already felt that this was my dream job. It was awesome, and I totally loved it. I found people even more collaborative in companies than they were in university research – we were all working to help the company do well and make a dent in the universe. In academia you also try to make a dent, but it was very often your own dent. Academia becomes a very competitive world since you have to make a name for yourself to succeed. Not that this doesn’t happen in the business world but teamwork, in my experience, is more highly valued because it really does take a significant effort from many people to make something interesting happen.

On Jonathan's Transition from LinkedIn to Data Science Consulting to Intuit

After his tenure at LinkedIn, Jonathan Goldman started a data science consulting firm, Level Up Analytics with Lucian Lita his wife Anuranjita Tewary. His company Level Up eventually became acquired by one of their clients, Intuit. Jonathan is currently Director of Data Science and Analytics at Intuit.

The three of us saw this opportunity — the demand for data science and building technology that would help solve data science problems. We saw a huge need that was just constant, and thought we could build a premier consulting firm and we would go to these companies and help them transform their businesses, while hiring people that we really liked working with.

The amazing thing was that we were able to get really good talent, get really good clients, and work on really challenging problems. There weren’t that many people doing exactly what we were doing — no-one else did the full end-to-end, including “What’s the business problem you’re facing? Where’s the place we can have the most impact? What technology might need to be built or deployed? What algorithms and analysis need to be done? We could do the full stack — I think a lot of companies really liked that approach. 
One of our clients, Intuit, after we got to know them and they got to know us, approached us about getting our entire company focused on Intuit — namely they wanted to acquire us. We really liked the problem they were working on. They were fundamentally changing people’s lives by making it easier to manage their finances, do their taxes and run a small business. It’s actually quite an interesting problem because they see so much of the economy. They are really truly one of the few companies that I think is mapping the world’s economy. You could say that LinkedIn is mapping the talent economy, but Intuit is actually mapping the real transactions that are happening. I don’t know any other company that has such interesting data. The impact on the economy and economic wealth is profound.  To me, it was a good mission to be a part of, and I really liked the culture and the people.

This is an excerpt from Jonathan's full interview in the upcoming Data Science Handbook.  Follow the link to download three chapters for free!

What is a data scientist's career path?


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