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eDiscovery Leaders Live: Irina Matveeva, Isha Chopra, Paul Jakubik, & Dinesh Karamchandani of Reveal

George Socha
George Socha

eDiscovery Leaders Live: Irina Matveeva, Isha Chopra, Paul Jakubik, & Dinesh Karamchandani of Reveal

 

Irina Matveeva, the head of Reveal’s data science team, and team members Isha Chopra, Paul Jakubik, and Dinesh Karamchandani join George Socha, Senior Vice President of Brand Awareness at Reveal, for ACEDS #eDiscoveryLeadersLive.

For this week’s episode, we asked Dr. Matveeva and several of her data science team members to share their experiences and thoughts.

Dr. Matveeva is responsible for Reveal’s data science organization and applying machine learning and natural language processing approaches throughout the Reveal platform. She is an Adjunct Professor at the Illinois Institute of Technology (IIT) and has nearly a decade of both practical and academic experience in natural language processing. Dr. Matveeva received her Ph.D. from the University of Chicago. She co-chaired the TextGraphs workshops in 2012, 2011, 2008, and 2007, and is a reviewer for multiple prestigious journals and publications.

Isha Chopra is a Sr. Data Scientist at Reveal and a Team Lead. She has a MS in Computer Science from IIT and a Bachelor of Technology in Computer Science Engineering from Jaypee University of Information Technology. Isha has been with Reveal for 7 years.

Paul Jakubik is a Senior Principal Architect at Reveal. Paul has a BA in Computer Science from Rice University. He has been with Reveal nearly 15 years.

Dinesh Karamchandani is at Data Scientist at Reveal. Dinesh holds an MS in Computer Science from IIT and a bachelor’s degree in Computer Engineering from the University of Mumbai. He has been at Reveal for 2½ years.

Key Highlights

  • [2:53] Their careers at Reveal: when each of them joined, in what capacity, and how things have developed since then.
  • [7:28] The benefits of building actual products.
  • [9:47] Areas of special interest for each guest.
  • [13:03] How the data science team at Reveal works together to improve the product.
  • [16:01] Managing the data science team.
  • [18:37] How the data science team members work effectively with colleagues throughout Reveal.
  • [20:57] How data science team members lead partnership with Reveal clients.
  • [22:32] Helping law firms build new capabilities.
  • [25:32] How they stay on top of the technology.
  • [28.59] How to become a data scientist at Reveal.
Key Quotes by the Reveal Team

    • Irina: “Every project is almost like a start-up. You have to research. You have to come up with an idea, prototype it, and some things will not work out, but some things will. You always need to adjust and adapt to tune it to the particular needs of our industry, of our clients.”
    • Isha: “I started my journey with Reveal as an intern, I did my master’s in computer science, had a few years of software development and experience and that’s when I joined Reveal. It’s been a great learning experience. The team has been growing. There’ve been some very good projects we’ve been working on of late, and as I mentioned there’s a lot of collaboration across teams so you get to learn not just about your workings day to day but you also get to interact with other members of the company which is amazing. What I really find interesting is a lot of growth opportunities with Reveal and getting to work on real-world projects.”
    • Paul: “I’ve been at companies that had strong developers and no domain knowledge, and they failed pretty fast. I’ve been at companies that had lots of domain knowledge and weak developers, they got a lot further but still had all kinds of problems. Here we have both the domain knowledge and the strong developers and that means we get to solve real problems, make real impact to customers lives. Having real results that really affect people and make people happier – that’s everything I could ask for in software development.”
    • Dinesh: “When we do work with a client, you need to understand the problems that the client has, which is where the model building aspect of it comes and which has been a very fun, rewarding process for me: looking at these very broad categories of problems and dividing them into smaller chunks, smaller pieces, and then working on them using everyone’s help to gain a different perspective on how we can solve a particular problem.”

Connect with Irina, Isha, Paul, & Dinesh

 

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