04-18, 16:05–17:05 (US/Eastern), Auditorium 5
During this expert panel, we'll explore the critical intersections of data science, engineering, and stakeholder engagement in today's organizations. This discussion will address how to break down silos between technical disciplines, establish effective collaboration models, create rapid experimentation frameworks, and successfully transition projects from exploration to production. Our panelists bring diverse perspectives on building integrated teams that balance innovation with enterprise standards while delivering real value.
This panel brings together practitioners and leaders to discuss the evolving landscape of data science collaboration and implementation. As organizations face increasing pressure to derive value from AI/ML initiatives, the traditional boundaries between disciplines are being reexamined and redefined.
Our panelists will explore:
- Breaking down isolation between data scientists, MLOps engineers, developers, and other stakeholders
- Creating effective frameworks for rapid experimentation that balance innovation with enterprise standards
- Establishing robust handoff processes for transitioning models from exploration to production
- Bridging cultural divides between the explorative nature of data science and the engineering mindset of MLOps
- Practical strategies for cross-functional collaboration that leverages complementary skills
- Managing stakeholder expectations and improving communication with non-technical audiences
This discussion is designed for data professionals at all levels—from individual contributors to team leaders and executives—who are navigating the challenges of modern data science implementation. The panel will address both technical and organizational aspects of successful data science teams.
The session will include time for audience Q&A, allowing attendees to engage directly with panelists about their specific challenges in building collaborative data science environments.
No previous knowledge expected
Chris has more than 30 years experience in the space, from analytics to senior management. He learned long ago that it is people skills, not his technical ability nor his fashion sense, that would help him make an impact. He remains glad that every day is a surprise.
Thomas is a senior machine learning engineer at GoHealth, where he builds and productionizes GenAI models. Previously, he worked in consulting and at a technology startup, focusing on MLOps adoption. He originally came from the statistics and data science side, but has also worked in software and data engineering, searching for lessons from these more mature disciplines for how to create maintainable and scalable software systems. Now, Thomas is passionate about integrating these diverse insights to build robust ML systems.
With over a couple of decades of experience in Information Technology, I have worked on groundbreaking technologies like Cloud and Machine Learning and witnessed their impact on the business and society. I am currently working as an Associate Director in Software development at S&P Global, one of the leaders in Financial Services. I am leading a team that contributes to the AI initiatives of S&P Global. I also hold a Masters degree from UVA in Data Science
LinkedIN: https://www.linkedin.com/in/mani-shanmugavel/
Medium: https://medium.com/@manikrajan
Renee Teate is the Senior Director of Data Science at higher ed tech company HelioCampus and author of SQL for Data Scientists (Wiley). Many people know her as the host of the Becoming a Data Scientist Podcast, or as "Data Science Renee" from BlueSky (previously becomingdatasci on Twitter).
Renee lives in Harrisonburg, VA, and is a graduate of JMU and UVA. She has worked with data her entire career, as a database designer, data analyst, data scientist, and director. She enjoys chatting with people looking to "break into" data careers, or looking to build their data science network.