Preferred Qualifications: • PhD in computer science, statistics, economics or related fields • Expert understanding of statistical methods and skills such as Bayesian Networks Inference, linear and non-linear regression, hierarchical, mixed models/multi-level modeling • Strong experience with R, RSTudio, Python, SAS, SQL, NoSL • Up-to-date knowledge of machine learning and data analytics tools and techniques • Strong knowledge in predictive modeling methodology • Experienced at leveraging both structured and unstructured data sources • Willingness and ability to learn new technologies on the job • Demonstrated ability to communicate complex results to technical and non-technical audiences • Strategic, intellectually curious thinker with focus on outcomes • Professional image with the ability to form relationships across functions • Ability to train more junior analysts regarding day-to-day activities, as necessary • Proven ability to lead cross-functional teams • Strong experience with Cloud Machine Learning technologies (e.g., AWS Sagemaker) • Strong experience with machine learning environments (e.g., TensorFlow, scikit-learn, caret) • Demonstrated Expertise with at least one Data Science environment (R/RStudio, Python, SAS) and at least one database architecture (SQL, NoSQL) • Financial Services background #LI-NG1 #LI-Onsite Exempt Status: (Yes = not eligible for overtime pay) ( No = eligible for overtime pay) Workplace Type: Our Approach to Office Workplace Type Certain positions outside our branch network may be eligible for a flexible work arrangement. Basic Qualifications: • Masters degree in computer science, statistics, economics or related field • 5+ years of experience related work experience using statistics and machine learning to solve complex business problems, experience conducting statistical analysis with advanced statistical software, scripting languages, and packages, including experience with big data analysis tools and techniques, and building and deploying predictive models, web scraping, and scalable data pipelines.