Professional Development
AI / Career development / Data / Federal government / Tech jobs

How I Got Here: LinkedIn research took this data scientist from academia to Accenture’s C-suite

Viveca Pavon-Harr oversees over 800 workers and sees herself as a scientist who relies on technology. Here’s how she pivoted into the field — and her advice for other aspiring data pros.

Viveca Pavon-Harr. (Courtesy Accenture)

This is How I Got Here, a series where we chart the career journeys of technologists. Want to tell your story? Get in touch.

When it comes to science and tech, Viveca Pavon-Harr sees herself as a little bit of both. But at the start of her career, she didn’t see either.

At 38, the Columbia Heights, DC resident was just appointed chief data scientist of Accenture Federal Services (AFS), the govtech arm of Accenture. On the day-to-day, she oversees about 800 data scientists and analytics workers and manages 75 folks directly at the firm’s Applied Intelligence Discovery Lab.

When the day is done (or often during — she’s a big treadmill desk user) you can find her running or playing tennis and, yes, completing some models based on the sports data. sat down with Pavon-Harr to talk about her journey, the world of data science and what she thinks is next in the govtech space. This interview has been edited for length and clarity.

What does the job of a chief data scientist look like?

Viveca Pavon-Harr: We have a very long list, over 135 lists, of data and analytics projects across AFS. So my role is to get an understanding of what’s happening across the board. What are we delivering? What are we doing? Making sure that we’re passing the quality control and quality assurance of those projects; making sure that our data scientists are staying up to speed on their skill sets and that we’re providing opportunities for growth and development for them. Making sure that the projects have the right people aligned to them and that, as projects evolve, we either allow people to evolve and grow within the same projects or we bring in the right subject matter expertise. Then it’s sharing a lot of the goodness of what we do.

Where did you go to school? And what did you study?

I did my undergrad at Texas A&M Commerce. I have an undergraduate in econ with minors in international relations and finance. I went to get an MBA, and my MBA was very much focused on economics, and then I did a lot of geospatial work. I have a Ph.D. in public policy and political economy from the University of Texas at Dallas.

What made you want to get into tech and data science?

I was hoping to be an academic. I got all these degrees and I did all these things just so I could teach at a university one day. I was doing all the right research and I was having a lot of fun, actually. My research was on organized criminal groups in Latin America, so that meant drug traffic and organization, gangs, and those types of groups, and I focused very much on the Central American region.

I was doing a lot of my research in Spanish and I got a postdoc from the University of Arizona and after a while there I just decided that that wasn’t my calling. It wasn’t my passion. So I took all my skill sets and I was like, “All right, let’s see what Viv is good at,” I put everything on an Excel sheet, as one does, and I fed it to LinkedIn. I let LinkedIn tell me what I was, and LinkedIn said “Well, you’re a data scientist.” I came into Accenture, which gave me a great opportunity to start as a data scientist, hands on the keyboard, boots on the ground, and that’s how I started.

When you ask me how did I get into it — I dove straight in with about knowing what I was getting into because I wanted to solve a problem. That’s why I had to ask LinkedIn what I was good at because I didn’t even know at the time that that was natural language processing or that was data science.

How did your career progress from your first role?

I went from being an academic at the University of Arizona and then came straight to Accenture. I was a data scientist, so I was an analyst, and I was just running models, burning my computer and burning the midnight oil making sure that the models were running accurately in time.

[Being a data scientist] I meet a lot of the requirements from different aspects of being able to do the analytics, do the hard data science problems, but also be able to converse about it in a very simple way. If I have to talk about a superpower, my superpower is really making very complex issues totally understandable to the end user and relatable to the end user. I don’t think of data science being this really black box kind of scenario. I think it should be very comprehensible for all of us.

So, when I came to Accenture, I came in as an analyst to the Discovery Lab, which is our Analytics Innovation Hub, and I was quickly promoted to an engagement lead because I was a person who could help translate complex data science to our clients, and that’s a lot of what we were doing. From there, I became the deputy director of the lab to the director of the lab and now to the chief data scientist of the company.

What’s it like managing so many people?

I really love my job. I think that I work with the smartest people I’ve ever met. There is a level of passion that I admire very much from the team and I think that we all feed off of each other on doing really exciting, nerdy, super geeky things that are solving complex problems that we all love and thrive on. We feed off of each other, with that energy of making sure that we’re encouraging each other like, “That model is great! You’re almost there!” We push each other to be better.

I know that my job is important, and I know that the way we have to do the job is very important and I take that very seriously. I don’t think I’m that important. I think that being a team player and part of a team that actually does really cool things is probably the most important role that I have.

What advice would you give to aspiring data scientists and technologists?

Never stop learning. If generative AI has taught us anything, it is that this space is prime for innovation. It has been designed for growth and we are just at the beginning of this ride. Unless you’re willing to have an open mind to continue to grow, to continue to develop, to continue to expand your skill sets, this is probably not the right space for you.

I think today we have an evaluation of a new LLM, a large language model, about every six weeks, which is crazy to think of. But that also means that we have people who are constantly testing out new things, new models, new technologies, new companies, new implementations, which is a lot of fun, but it’s also a lot of learning and a lot of collaboration. So if you’re coming into this, be willing to learn, be flexible to the new things, and that’s going to be a huge driver for success.

Companies: Accenture
Series: How I Got Here

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