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In this episode, we talked with Abouzar Abbaspour, a data engineer whose career spans software engineering in Iran, building crowd and recommendation systems at a Dutch theme park, deploying large-scale ML models at Bol.com, and now working at Tesla. Abouzar shares how he bridged diverse industries, tackled real-world data challenges, and adapted to new roles while keeping a hands-on approach to machine learning and engineering.TIMECODES00:00 Career journey and early motivations06:17 Moving to Europe for data science12:18 Working with theme parks and crowd modeling18:29 Lessons from ride and visitor data23:06 Building recommendation systems at Efteling27:26 Joining Bol.com and the Dutch e-commerce industry32:49 Product and brand recommendation logic36:09 Experimenting with "Tinder for brands"40:26 Engagement metrics and product validation43:02 From ML engineering to data engineering roles52:04 Hands-on skills at Tesla and industry expectations57:43 Career growth, learning, and adviceConnect with AbouzarLinkedin -   / abouzar-abbaspour   Website - https://www.abouzar-abbaspour.com/ Connect with DataTalks.Club: Join the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/...Check other upcoming events - https://lu.ma/dtc-eventsGitHub: https://github.com/DataTalksClubLinkedIn -   / datatalks-club   Twitter -   / datatalksclub   Website - https://datatalks.club/

​A conversation with Abouzar Abbaspour on ML engineering, data pipelines, and real-world impact ​ ​What does it take to turn raw data into products that millions of people love to use? Abouzar Abbaspour will discuss this based on his experience working with startups, amusement parks, e-commerce, and now Tesla.

​At Efteling theme park, he built queue-time forecasting and recommendation systems for visitors. At bol.com, he helped deploy a recommendation engine to over 6 million users. And today at Tesla, he works on predictive maintenance and integrates LLM agents into production systems.

​In this conversation, Abouzar shares stories from the front lines of data and ML engineering: what succeeds, what fails, and what matters when you move from experiments to production. ​ ​We plan to cover:

• ​Forecasting queues and building recommendation systems at a theme park
• ​Deploying large-scale recommender systems at bol.com
• ​The leap from data engineering into ML engineering
• ​Predictive maintenance and LLM agents at Tesla
• ​Why productionizing ML is about much more than the model
• ​Which trends in data and ML are hype, and which are here to stay

​ ​About the speaker

​Abouzar Abbaspour is a machine learning and data engineer whose career spans startups, academia, e-commerce, theme parks, and automotive. He co-founded a telecom startup in Iran, worked on forecasting models and recommendation engines at Efteling, and later deployed large-scale ML models at bol.com. Today, at Tesla, he focuses on predictive maintenance, LLM agents, and scalable pipelines. Abouzar holds an EngD in Data Science from Eindhoven University of Technology.

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