Don’t Be Afraid To Build Your Brand | Srivatsan Srinivasan on The Artists of Data Science podcast

On this episode of The Artists of Data Science, we get a chance to hear from Srivatsan Srinivasan, a data scientist who has nearly two decades of applying his intense passion for building data driven products.

He’s a strong leader who effectively motivates, mentors, and directs others, and has served as a trusted advisor to senior level executives. He gives insight into how he broke into the data science field, the importance of focusing on business outcomes,, and some important soft skills.

Srivatsan shares with us his tips on how to navigate crazy job descriptions, as well as his methods for communicating with executives. This episode contains actionable advice from someone who has been working with data since the beginning!

Some notable segments from the show

[10:26] What it means to be a good leader in data science

[11:45] How to productionize a model

[17:54] How to navigate difficult job descriptions

[20:33] Tips on communicating with executives

Where to listen to the show

Listen to the episode on Apple Podcasts, Spotify, Overcast, Stitcher, Castbox, Google Podcasts, TuneIn, YouTube, or on your favorite podcast platform.

Srivatsan’s journey into data science

From there, he transitioned into working in the ETL world, and then working with big data. Whenever he worked with customers, he noticed that he had to work with large datasets, leading him into data science and machine learning.

Initially, Srivatsan and his team had many failures in their first project, but it was a great learning experience for him. Eventually, he redid the project and succeeded, marking the inception of his data science career.

Breaking into the field was kind of a gradual transition. So I’ve been in the data space from the beginning. Not in the data science though. I’ve been working with data from the starting of my career.

Where is the field headed in 2–5 years?

Research: There is a lot of activity currently on advanced algorithms. Srivatsan sees an increase in accuracy in these models overtime, with the insights being democratized.

Model Explanation: Sometimes complex models lose their ability for explanation. So he sees more adoption from an enterprise standpoint, which will lead to more models that become accessible to the end-user.

[6:53] So when we talk about where the field is headed, right. There are two aspects of it. The very the very first aspect is the research side of it, right. There’s a lot going on in the research world on advanced algorithms and everything. The key thing is like you have a lot of technology companies sitting over there like Amazon, Microsoft, and Google. They have a lot of data at their disposal. And they are trying to create like are pretty accurate systems for complex jobs. The complex job can be speech to text, or it can be OCR. It’s typically not accessible to the industry, right. Industry does not have that much data to train a translation model, or a speech to text model. So what I see is the accuracy over time for these models, will get better, but the insights will be democratized. So you’ll see this as cloud services running around and accessible to the industry. That is one aspect of it. The second maybe the model explanation aspect of it. As we go into the complex model we lose the explanation capability of it. So there will be a lot of research is going on, that is on the research side of it. But in the industry side of it, there a lot of initiatives that are getting started; but more in POC stages. The adoption is not completely federated across enterprise. So what I see is more and more enterprise line of business will adopt the more of these techniques and then you can see like that fuels a new way of adoption industry. So that’s what I see like in two to five years. It’s more like more adoption and more like models getting more accessible to end-users. Like complex models like speech to text and it’s still that. But when you really use it in industry, you don’t get that accurate models. So what I meant, it would become more accurate.

What will separate great data scientists from the rest of them?

[8:51] When we say how you adopt your data science journey, we typically — we are more focused on today algorithm and technology, the real focus should be on business outcome.It does not matter whether you use tensorflow or pytorch to solve a problem. It’s about how you are solving a problem and delivering in business outcomes. Right. That should be the clean focus of it. I think more and more data scientist today are technology focused. They need to use technology to just solve a problem. Right. So they should more focus on business outcomes. And that’s what, like, will really differentiate the good and best data scientist.

Key takeaways from the episode

Concept drift

Tips on communicating with executives

Important soft skills

What to do with these crazy jobs descriptions

Memorable quotes

[9:09] “I think more and more data scientists today are technology focused. They need to use technology to just solve a problem…they should focus more on business outcomes.”

[10:26] “…a good leader in data science…should be ready to embrace failure”

[12:21] “…start with modularizing your code, see where are your common functions that you can use”

The one thing that Srivatsan wants you to learn from his story

From the lightning round

The best advice Srivatsan has ever received

Advice that Srivatsan would give his 20 year self

A topic outside of data science Srivatsan thinks we should study

[22:48] Connect with the industry leaders. Send them a note, and ask for a 10–15 minute chat.

Srivatsan’s book recommendation



Episode Transcript

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The transcript for this episode can be found here.

Full Episode on YouTube

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