#158 Part 1- How do we get data scientists going in 8 hours with Nitish Mathew Global Head of Data Engineering at Afterpay

#158 Part 1- How do we get data scientists going in 8 hours with Nitish Mathew Global Head of Data Engineering at Afterpay

Author: Felipe Flores June 15, 2021 Duration: 31:01

When Global Head of Engineering, Nitish Mathew looks back at his journey over the last four years with Afterpay, he describes it as a phenomenal ride and a tremendous learning journey. They have had to reinvent themselves three times in a very real way, getting rid of their first and second platform and building with the view of not assuming that this is for the next 10 years, but building with the view that they will need to get rid of this in two to three years.

Nitish says when a data scientist joins a company they should be able to query a data lake or data warehouse like any library. We need to help users find information quickly so they can actually get the information that they want to do their research immediately. It needs to be organised so people know how to find what they need.

We end part one with a discussion on how organisations should use coaching to get the best results. Ultimately, leadership's role is to get the right outcomes and support people with coaching, technology, decision making and resources.  He believes the goal is not to build the fastest product. The goal is to make sure your data scientist colleagues are happy. And for them to be happy, you need to make sure that she or he is able to do their job fast, which involves giving proper easy to use performance tooling, giving good data, and then making sure that on a daily basis, it works.

Enjoy the episode!

Read the full episode summary here: Ep 158

Thanks to our sponsor Talent Insights Group!


In a field dominated by discussions of algorithms and infrastructure, Data Futurology carves out a different, crucial space. Host Felipe Flores guides conversations toward the human-centric challenges that ultimately determine whether an AI initiative succeeds or fails. This isn't a technical deep dive into model architectures; it's a series of dialogues about strategy, organizational change, and the practical leadership required to bridge the gap between potential and real-world impact. You'll hear from practitioners and executives who have navigated the complex last mile of deployment, where the real work of integrating technology into business processes and culture happens. The podcast explores how to select the right problems, build effective teams, and cultivate an ethical, forward-thinking approach to data science and machine learning. For leaders, managers, and anyone responsible for steering their organization through the adoption of these powerful tools, Data Futurology offers grounded insights and actionable perspectives. It’s about moving beyond the hype to create sustainable value, ensuring that the rapid pace of advancement in artificial intelligence is matched by thoughtful and effective human leadership. Tune in for a necessary complement to the more code-focused shows in your feed.
Author: Language: English Episodes: 100

Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science
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