Must have any experience with or knowledge of i designing and building highly scalable distributed ML models; ii MLOps best practices and tools; iii refactoring code for scalability, efficiency and identifying bottlenecks in ML training; iv Python and PySpark, including pandas, numpy, scikitlearn, geopandas, and Tensorflow; v R programming language including ggplot2, dplyr, and statistical modeling packages; vi Distributed SQL querying frameworks such as HIVE, Impala and BigQuery; vii AWS tools and technologies; viii JIRA and Confluence project management tools; ix building and testing ETL pipelines; x presenting results of AIML models to nontechnical stakeholders; xi targeting, ranking and recommender systems; xii creating and managing CICD pipelines; xiii comparing and validating model performance; xiv creating KPIs and performance metrics; xv software engineering with code reviews; and xvi experimental design and AB testing. Experience can be concurrent.
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