Vijay Yadav (Director of Data Science at Merck) joins me to chat about a very interesting project he launched at Merck involving LLMs in production. A big part of this discussion is how to make data ready for generative AI.

This is a great example of an LLM-native use case in production, which are rare right now. Lots to learn from here. Enjoy!

LinkedIn: https://www.linkedin.com/in/vijay-yadav-ds/

In 2023, businesses are relying more heavily on data science and analytics teams than ever before. However, simply having a team of talented individuals is not enough to guarantee success.  In the last of our RADAR 2023 sessions, Vijay Yadav and Vanessa Gonzalez will outline the keys to building high-impact data teams in 2023. They will discuss what are the hallmarks of a high-performing data team, the importance of diversity of background and skillset needed to build impactful data teams, setting up career pathways for data scientists, and more. Vijay Yadav is a highly respected data and analytics thought leader with over 20 years of experience in data product development, data engineering, and advanced analytics. As Director of Quantitative Sciences - Digital, Data, and Analytics at Merck, he leads data & analytics teams in creating AI/ML-driven data products to drive digital transformation. Vijay has held numerous leadership positions at various companies and is known for his ability to lead global teams to achieve high-impact results.  Vanessa Gonzalez is the Sr. Director of Data Science and Innovation at Businessolver where she leads the Computational Linguistics, Machine Learning Engineering, Data Science, BI Analytics, and BI Engineering teams. She is experienced in leading data transformations, performing analytical and management functions that contribute to the goals and growth objectives of organizations and divisions.  Listen in as Vanessa and Vijay share how to enable data teams to flourish in an ever-evolving data landscape. 

Today I sit down with Vijay Yadav, head of the data science team at Merck Manufacturing Division. Vijay begins by relating his own path to adopting a data product and UX-driven approach to applied data science, andour chat quickly turns to the ever-present challenge of user adoption. Vijay discusses his process of designing data products with customers, as well as the impact that building user trust has on delivering business value. We go on to talk about what metrics can be used to quantify adoption and downstream value, and then Vijay discusses the financial impact he has seen at Merck using this user-oriented perspective. While we didn’t see eye to eye on everything, Vijay was able to show how focusing on the last mile UX has had a multi-million dollar impact on Merck. The conversation concludes with Vijay’s words of advice for other data science directors looking to get started with a design and user-centered approach to building data products that achieve adoption and have measurable impact.

In our chat, we covered Vijay’s design process, metrics, business value, and more: 

Vijay shares how he came to approach data science with a data product management approach and how UX fits in (1:52) We discuss overcoming the challenge of user adoption by understanding user thinking and behavior (6:00) We talk about the potential problems and solutions when users self-diagnose their technology needs (10:23) Vijay delves into what his process of designing with a customer looks like (17:36) We discuss the impact “solving on the human level” has on delivering real world benefits and building user trust (21:57) Vijay talks about measuring user adoption and quantifying downstream value—and Brian discusses his concerns about tool usage metrics as means of doing this (25:35) Brian and Vijay discuss the multi-million dollar financial and business impact Vijay has seen at Merck using a more UX  driven approach to data product development (31:45) Vijay shares insight on what steps a head of data science  might wish to take to get started implementing a data product and UX approach to creating ML and analytics applications that actually get used  (36:46)

Quotes from Today’s Episode “They will adopt your solution if you are giving them everything they need so they don’t have to go look for a workaround.” - Vijay (4:22)

“It’s really important that you not only capture the requirements, you capture the thinking of the user, how the user will behave if they see a certain way, how they will navigate, things of that nature.” - Vijay (7:48)

“When you’re developing a data product, you want to be making sure that you’re taking the holistic view of the problem that can be solved, and the different group of people that we need to address. And, you engage them, right?” - Vijay (8:52)

“When you’re designing in low fidelity, it allows you to design with users because you don’t spend all this time building the wrong thing upfront, at which point it’s really expensive in time and money to go and change it.” - Brian (17:11)

"People are the ones who make things happen, right? You have all the technology, everything else looks good, you have the data, but the people are the ones who are going to make things happen.” - Vijay (38:47)

“You want to make sure that you [have] a strong team and motivated team to deliver. And the human spirit is something, you cannot believe how stretchable it is. If the people are motivated, [and even if] you have less resources and less technology, they will still achieve [your goals].” - Vijay (42:41)

“You’re trying to minimize any type of imposition on [the user], and make it obvious why your data product  is better—without disruption. That’s really the key to the adoption piece: showing how it is going to be better for them in a way they can feel and perceive. Because if they don’t feel it, then it’s just another hoop to jump through, right?” - Brian (43:56)

Resources and Links:  LinkedIn: https://www.linkedin.com/in/vijyadav/

The data journey is a slow painstaking process. But knowing where to start and the areas to focus on can help any organization reach its goals faster.

Today’s guest, Vijay Yadav, Director of Quantitative Sciences & Head of Data Science at the Center for Mathematical Sciences at Merck, explains the 6 key elements of data strategy, complete with advice on how to navigate each.

Join us as we discuss:

The different components of a data strategy Shifting mindset within the C-Suite Structuring the operating model Enabling people to work with data at scale Most effective tactics to kickstart a community around data science

Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn.

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