Luigi Patruno, VP of Data Science at 2U, Inc, shares insights on managing data products and the challenges that come with it. He discusses effective communication, transitioning from an individual contributor to a data science leader, the importance of discipline and focus in goal-setting, data science leadership and incentives, the value of specific and meaningful positive reinforcement, setting boundaries as a manager, and the importance of solutions and identifying blind spots.
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Quick takeaways
The data science team at 2U is organized into specialized sub-teams, some operating in a centralized project-based model and others embedded within product squads, with a separate ML engineering team managing the ML platform.
The data science team at 2U acts as change agents within the organization, using a product management approach to collaborate with stakeholders, streamline processes with a handbook, and focus on creating a turnkey operation.
The data science team at 2U emphasizes the importance of aligning incentives with business objectives, measuring tangible business impact and return on investment, and continuously adapting and learning to overcome challenges.
Deep dives
Organizational Structure and Focus
The podcast episode explores how the data science team at 2U is organized and focused. The team is divided into sub-teams that specialize in different areas such as marketing and learning marketplace. Some sub-teams work in a centralized project-based model, while others are embedded within product squads. There is also a separate ML engineering team responsible for building and maintaining the ML platform used by the data scientists. The podcast highlights the importance of having a well-defined process and a turnkey operation in order to ensure efficiency and success. The team uses a third-party ML platform and is connected to the data platform through Snowflake and other tools.
Change Management and Collaboration
The podcast discusses the role of the data science team as change agents within the organization. They focus on helping other teams become more efficient and capitalize on new opportunities. The team uses a product management approach to collaborate with stakeholders and understand their problems. The team emphasizes the need for buy-in and commitment from stakeholders to ensure the success of projects. The implementation of a handbook has been instrumental in streamlining processes and reducing the need for repetitive explanations. The data science team aims to create a turnkey operation by standardizing processes and ensuring quality outcomes.
Incentivizing Success and Measuring Impact
The podcast delves into the importance of incentives and measuring impact in the data science team. The host and guest discuss the need to align incentives with business objectives and to communicate in a language that stakeholders understand. The guest explains how he evaluates the team's success based on tangible business impact and return on investment. The team uses an ML platform that enables data scientists to work efficiently, and the ML engineering team plays a crucial role in integrating solutions into the wider software ecosystem. The podcast emphasizes the continuous learning process and the importance of adapting to overcome challenges.
Importance of Stakeholder Investment and Early Evaluation
One of the main insights discussed in the podcast is the importance of evaluating stakeholder investment and commitment early on in a project. By determining how invested the stakeholder group is in solving a problem and spending time with the team, potential problems can be addressed before significant resources are allocated. This allows for a more efficient use of individual contributor's time and ensures that the team is working on the right problems from the start, thus increasing the likelihood of success.
Focus and Discipline in Goal Setting and Processes
Another key point discussed in the episode is the significance of focus and discipline in managing a data science team. The use of quarterly goal setting with specific objectives and key results (OKRs) helps provide clarity and focus, ensuring everyone knows what needs to be accomplished. Additionally, implementing processes and a data science handbook helps maintain discipline and consistency in project outcomes. By setting clear expectations, developing standardized processes, and holding individuals accountable for their specific responsibilities, variability and ambiguity are reduced, leading to more consistent and successful outcomes.
MLOps podcast #185 with Luigi Patruno, VP of Data Science at 2U, Inc, Lessons on Data Science Leadership.
// AbstractPicture this: you've got data products to manage, and you're in charge of a team. It's not all sunshine and rainbows, right? Luigi dives into the nitty-gritty of the challenges - from juggling data projects to wrangling the team dynamics. It's a real adventure, let me tell you!
// Bio
Luigi Patruno is a results-driven data science leader passionate about identifying value-add business opportunities and converting these into analytical solutions that deliver measurable business outcomes. As a leader he focuses on defining strategic vision and, through motivation and discipline, driving teams of highly quantitative data scientists, machine learning engineers, and product managers to achieve extraordinary results. He is currently the VP of Data Science at 2U, where he leads the data science department focused on optimizing business operations through advanced analytics, experimentation, and machine learning. He enjoys teaching others how to leverage data science to improve their businesses through public speaking, teaching courses, and writing online at MLinProduction.com.
// MLOps Jobs board
https://mlops.pallet.xyz/jobs
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
Website: https://mlinproduction.com/
YouTube channel: https://www.youtube.com/playlist?list=PLBLnN4jzkyqkjLIRpDNZcsG7TMMEk9Asa
High Output Management book by Andrew Grove: https://www.amazon.nl/-/en/Andrew-S-Grove/dp/0679762884The One Minute Manager by Kenneth Blanchard Ph.D. and Spencer Johnson M.D.: https://www.amazon.com/Minute-Manager-Kenneth-Blanchard-Ph-D/dp/074350917X
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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Luigi on LinkedIn: https://www.linkedin.com/in/luigipatruno91/
Timestamps:
[00:00] Luigi's preferred coffee
[00:30] Takeaways
[03:04] Being practical
[05:44] Data-Driven Decision-Making in Management
[12:53] Recent Team Win
[14:43] The perfect storm
[20:22] Change Management and ROI
[25:09] Change Management: Navigating Resistance
[29:59] Clarifying North Star Communication
[36:24] OKRs in Data Science
[40:47] Success Likelihood in Business
[45:08] Bus problem solution
[49:25] Data Science-Platform Collaboration
[53:19] Decentralized Platforms Explained
[54:38] Data Platform Architecture Overview
[57:14] Incentives for Team Motivation
[1:09:45] The blind spots
[1:12:22] Wrap up
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