· 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