In this episode, I sit down with Mark Freeman and Chad Sanderson (Gable.ai) to discuss the release of their new O’Reilly book, Data Contracts: Developing Production-Grade Pipelines at Scale. They dive deep into the chaotic journey of writing a 350-page book while simultaneously building a venture-backed startup. The conversation takes a sharp turn into the evolution of Data Contracts. While the concept started with data engineers, Mark and Chad explain why they pivoted their focus to software engineers. They argue that software engineers are facing a "Data Lake Moment, "prioritizing speed over craftsmanship, resulting in massive technical debt and integration failures.

Gable: https://www.gable.ai/

The Rise of the Data-Conscious Software Engineer: Bridging the Data-Software Gap | Mark Freeman | Shift Left Data Conference 2025

Data teams increasingly embrace software engineering practices to address quality and integration challenges, yet friction remains between software and data teams. This talk explores why standard practices alone aren’t enough and introduces the concept of the “Data-Conscious Software Engineer,” an emerging role critical to bridging these organizational divides. Attendees will learn how identifying and empowering engineers who deeply understand both software development and data workflows can foster stronger collaboration, improve data quality, and drive organizational change toward treating data as a strategic asset.

Mark Freeman joins me to chat about data contracts, the crazy life of being the first employee at a hot startup, writing books and creating content, and much more.

DataEngineering #Startups #AI #DataQuality #DataContracts

This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.

Please join the session that is best suited to your time zone...this topic is repeated at 2 different times. This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.

Presenter: Mark Freeman

Mr. Freeman is an executive data scientist with PhD-level education and over 25 years experience in advanced analytics and machine learning. As a Chief Data Scientist at IBM Consulting, he leads data science teams delivering production grade machine learning solutions to clients across multiple industries. He is a published author of advanced analytics research and principal patent author for optimal automated forecasting.

*** Please join us at the session that is best suited to your time zone. Note that this topic is: 1. Repeated at two different times to accommodate various time zones\, because it is 2. Posted simultaneously in multiple meetup groups world-wide *** It is recommended that you register at this Webex link ahead of time to receive a calendar invite and reminder. https://ibm.webex.com/weblink/register/r1ebfddfb7a40b4fbacd4e30bc006d29a

Please join the session that is best suited to your time zone...this topic is repeated at 2 different times. This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.

Presenter: Mark Freeman

Mr. Freeman is an executive data scientist with PhD-level education and over 25 years experience in advanced analytics and machine learning. As a Chief Data Scientist at IBM Consulting, he leads data science teams delivering production grade machine learning solutions to clients across multiple industries. He is a published author of advanced analytics research and principal patent author for optimal automated forecasting.

*** Please join us at the session that is best suited to your time zone. Note that this topic is: 1. Repeated at two different times to accommodate various time zones\, because it is 2. Posted simultaneously in multiple meetup groups world-wide *** It is recommended that you register at this Webex link ahead of time to receive a calendar invite and reminder. https://ibm.webex.com/weblink/register/r1ebfddfb7a40b4fbacd4e30bc006d29a

This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.

Please join the session that is best suited to your time zone...this topic is repeated at 2 different times. This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.

Presenter: Mark Freeman

Mr. Freeman is an executive data scientist with PhD-level education and over 25 years experience in advanced analytics and machine learning. As a Chief Data Scientist at IBM Consulting, he leads data science teams delivering production grade machine learning solutions to clients across multiple industries. He is a published author of advanced analytics research and principal patent author for optimal automated forecasting.

*** Please join us at the session that is best suited to your time zone. Note that this topic is: 1. Repeated at two different times to accommodate various time zones\, because it is 2. Posted simultaneously in multiple meetup groups world-wide *** It is recommended that you register at this Webex link ahead of time to receive a calendar invite and reminder. https://ibm.webex.com/weblink/register/r35a4b43d09dd200b049cf51dd4fce7c5

Please join the session that is best suited to your time zone...this topic is repeated at 2 different times. This session will provide an introduction to applications of machine learning to optimization. Optimization (often called prescriptive analytics) is a branch of data science that recommends the best actions for maximizing a desirable outcome (or minimizing an undesirable outcome). Modern applications often involve a combination of machine learning and mathematical programming. Attendees will get an introduction to modern applications of prescriptive analytics, illustrated through a variety of real world use cases. These use cases include optimizing treatments to maximize health outcomes, optimizing pricing to maximize profits, and optimizing maintenance operations to minimize cost. A review of these real world applications will enable attendees to explore how prescriptive analytics might contribute value to their own organizations.

Presenter: Mark Freeman

Mr. Freeman is an executive data scientist with PhD-level education and over 25 years experience in advanced analytics and machine learning. As a Chief Data Scientist at IBM Consulting, he leads data science teams delivering production grade machine learning solutions to clients across multiple industries. He is a published author of advanced analytics research and principal patent author for optimal automated forecasting.

*** Please join us at the session that is best suited to your time zone. Note that this topic is: 1. Repeated at two different times to accommodate various time zones\, because it is 2. Posted simultaneously in multiple meetup groups world-wide *** It is recommended that you register at this Webex link ahead of time to receive a calendar invite and reminder. https://ibm.webex.com/weblink/register/r35a4b43d09dd200b049cf51dd4fce7c5

In this episode, I interview Mark Freeman and talk about how he transitioned from public health to data science! We talk about what worked well in his journey, and what didn't, including a $20,000 investment gone sideways. Mark also gives some amazing LinkedIn job hacks! 

Connect with Mark on LinkedIn: https://www.linkedin.com/in/mafreeman2/ 

Check out opening's at Humu (Mark's company): https://boards.greenhouse.io/humu

Want to break into data science? Check out my new course coming out later this summer: Data Career Jumpstart - https://www.datacareerjumpstart.com

Subscribe on YouTube: https://www.youtube.com/channel/UCuyfszBAd3gUt9vAbC1dfqA

Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa