Time series data is everywhere — from inventory systems and energy grids to financial planning and product demand. As data volumes grow, the old ways of building individual forecasting models simply don't scale. How do you forecast hundreds of thousands of products without spending months on manual modeling? How do you know when to trust automation and when to step in? And what does it actually take to produce forecasts that business stakeholders will act on?
Rami Krispin is Senior Director of Data Science and Engineering at Apple Finance, where he leads teams working at the intersection of statistical modeling, machine learning, and production forecasting. He is the author of Hands-On Time Series Analysis with R, an open-source contributor, Docker Captain, and instructor. He holds an MA in Applied Economics and an MS in Actuarial Mathematics from the University of Michigan, where he began his journey learning time series on DataCamp — before going on to build his own course there.
In the episode, Richie and Rami explore time series foundation models and the case for scaling, traditional versus modern forecasting approaches, feature engineering in the business world, backtesting and model selection, risk management in automated forecasting, communicating forecast uncertainty to stakeholders, the evolving role of data scientists as architects, and much more.
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