This is a guest post co-authored by Taylor Names, Staff Machine Learning Engineer, Dev Gupta, Machine Learning Manager, and Argie Angeleas, Senior Product Manager at Ibotta. Ibotta is an American technology company that enables users with its desktop and mobile apps to earn cash back on in-store, mobile app, and online purchases with receipt submission, linked retailer loyalty accounts, payments, and purchase verification. Ibotta strives to recommend personalized promotions to better retain and engage its users. However, promotions and user preferences are constantly evolving. This ever-changing environment with many new users and new promotions is a typical cold start problem--there is no sufficient historical user and promotion interactions to draw any inferences from.

Are you looking to play an integral role in building something bigger? Ibotta is seeking a Senior Decision Scientist to join our analytics team and this opportunity will provide just that. The Senior Decision Scientist will help build out analyses that provide compelling, actionable and data-driven recommendations to internal and external stakeholders. As a Senior Decision Scientist, you will be responsible for using the latest developments in statistics, machine learning, and testing methodologies to understand the business in-depth, support and challenge strategic options, and build state-of-the-art tools to improve our largest line of business. Headquartered in Denver, CO, Ibotta ("I bought a…") is a free app that's transforming the shopping experience by making every purchase rewarding.

At Ibotta we train a lot of machine learning models. They make predictions for millions of users as they interact with our mobile app. While we do much of our data processing with Spark, our preferred machine learning framework is scikit-learn. As compute gets cheaper and time to market for machine learning solutions becomes more critical, we've explored options for speeding up model training. One of those solutions is to combine elements from Spark and scikit-learn into our own hybrid solution.

At Ibotta we train a lot of machine learning models. They make predictions for millions of users as they interact with our mobile app. While we do much of our data processing with Spark, our preferred machine learning framework is scikit-learn. As compute gets cheaper and time to market for machine learning solutions becomes more critical, we've explored options for speeding up model training. One of those solutions is to combine elements from Spark and scikit-learn into our own hybrid solution.