Citizens Financial Group, Inc. (CFG) seeks a Principal Data Scientist for its Westwood, MA location.

Duties: Partner with line of business pricing managers and product teams on the development of pricing strategy to achieve business growth objectives and maximize line of business risk-adjusted returns. Lead the development of statistical models to predict demand elasticity, application conversion, and other price-dependent performance measures in close partnership with business line pricing teams. Manage end-to-end implementation and ongoing maintenance of pricing models, including leading expert panel discussions with business partners, navigating the model governance process, and managing model back-testing and performance monitoring routines. Partner with Finance and Pricing teams on potential enhancements to forecasting and profitability models incorporating data-driven insights and output of price optimization models. Identify and recommend strategy changes to optimize profitability, volume growth, and/or other key performance measures. Serve as subject matter expert across business lines in price optimization. Regularly use discretion and independent judgment in a dynamic environment.

Requirements: Master’s degree in Accounting, Data Science, or closely related quantitative field and two (2) years of experience in the role or in a related position. Full term of experience must include: Utilizing relational database and statistical analysis programming languages and software, including SQL and Python, to extract high-volume and complex data to perform related analyses; Developing predictive models by implementing advanced data analysis and research techniques, including data quality checks, remedy techniques, statistical analysis, and inferences, data exploration analysis, data imputation, time series analysis, regression analysis, classification analysis, supervised and unsupervised learning techniques, formulating assumptions, and distilling business problems into analytical problems; Utilizing mathematical principles behind statistical methods to evaluate and interpret the results of predictive models reasonably; Utilizing advanced statistical programming using tools in Python to create complex reports and graphs to illustrate analysis; Conducting optimization tests, including feature selections, cross-validation, threshold selection, and hyperparameters tuning, using Python and data science tools; Leveraging big data and machine learning algorithms like ensemble trees and regression techniques to build models and measure model outcomes using key machine learning metrics, including AUC, average precision, precision and recall; Writing, maintaining, and improving production code for models and automation; and Utilizing Python IDE and Jupyter in cloud collaborative environments (including AWS and GCP) and code version control tools (including Bithub and Bitbucket).

May telecommute from any U.S. location.

Direct applicants only.

Equal Employment Opportunity

Citizens, its parent, subsidiaries, and related companies (Citizens) provide equal employment and advancement opportunities to all colleagues and applicants for employment without regard to age, ancestry, color, citizenship, physical or mental disability, perceived disability or history or record of a disability, ethnicity, gender, gender identity or expression, genetic information, genetic characteristic, marital or domestic partner status, victim of domestic violence, family status/parenthood, medical condition, military or veteran status, national origin, pregnancy/childbirth/lactation, colleague’s or a dependent’s reproductive health decision making, race, religion, sex, sexual orientation, or any other category protected by federal, state and/or local laws. At Citizens, we are committed to fostering an inclusive culture that enables all colleagues to bring their best selves to work every day and everyone is expected to be treated with respect and professionalism. Employment decisions are based solely on merit, qualifications, performance and capability.

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