·         Build and evaluate predictive and decision models to be deployed in production systems, or for research

·         Analysis of large amounts of historical data, determining suitability for modeling, data clean-up and filtering, pattern identification and variable creation, selection of sampling criteria, generating performance definitions and variables

·         Conducting experiments with different types of algorithms and models, analyzing performance, to identify the best algorithms to employ.

·         Architect and develop operational models that run at scale thru partnership with data engineer teams 

·         Master’s degree or higher in Statistics/Math/Computer Science or related field

·         Background in applied statistical modeling on large experimental or observational data sets

·         Experience extracting data from a variety of sources, and a desire to expand those skills (working knowledge SQL is required, Spark is a plus)

·         Experience with one or more statistical or machine learning software such as R, Python, etc.

·         Must showcase past work through published articles/GitHub/social media

·         6+ years of industry work experience in data scientist projects   

·          Knowledge of distributed computing systems, e.g. Cosmos, Spark, Hadoop, and relational database management system