Jacqueline Nolis is a data scientist and technical leader with 11 years of experience building and productizing machine learning solutions for enterprises including Microsoft, T-Mobile, Fanatics, and Expedia. She has led data science teams from founding stages to production, architecting large-scale systems—such as a Spark- and Docker-based model that scores favorite teams for 150M customers and runs daily for personalization and marketing. Her work spans optimization for ad bidding and campaign spend, NLP at scale serving millions of requests daily, and practical MLOps patterns that moved prototypes into production. Jacqueline blends hands-on model development (TensorFlow, PyTorch, RAPIDs, Dask) with product and UX-focused leadership from her CPO role, enabling teams and customers to deploy complex multi-GPU and cloud-native workflows. Known for mentoring across technical and stakeholder dimensions, she brings a PhD-level analytical background in industrial engineering to strategic business decisions at the executive level.
11 years of coding experience
12 years of employment as a software developer
PhD, Industrial Engineering, PhD, Industrial Engineering at Arizona State University
Master of Science, Applied Mathematics, Master of Science, Applied Mathematics at Worcester Polytechnic Institute
Generating pet names - creating an R Keras model and deploying it in a Docker container
Contributions:14 commits, 2 PRs, 13 pushes in 2 years 8 months
deployingpythonkeras-modeldockerdocker-container
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