Lead Data Scientist role at Mastercard (Finicity). Location: Salt Lake City, UT, United States; On-site. Mastercard is seeking a Lead Data Scientist to lead and grow the data science team, delivering production-grade machine learning services across financial applications. The ideal candidate will be a strong technical leader, able to mentor the team, plan projects, and communicate findings to executives and clients. Responsibilities: - Lead, mentor and expand the data science team and tech stack focused on production-grade services and capabilities. - Plan and direct data science / machine learning projects within the team. - Design and implement machine learning models for financial applications including transaction classification, temporal analysis, risk modeling from structured and unstructured data. - Measure, validate, implement, monitor and improve performance of both internal and external facing models. - Apply ML methods (SVM, Random Forest, XGBoost, LightGBM, CATBoost, etc), deep learning (LSTM, RNN, Transformer) and LLMs to analytical problems. - Propose creative solutions to challenges new to the company and the financial industry; communicate findings clearly to business leaders and clients. - Leverage best practices in ML and data engineering to develop scalable solutions. - Identify resource gaps and provide sustainable solutions to benefit the team. - Communicate vision and roadmap effectively; strong writing and public speaking skills. Requirements: - Experience mentoring and leading data science teams. - Substantial experience in data science/model develo

Lead Data Scientist role at Mastercard (Finicity). Location: Salt Lake City, UT, United States; On-site. Mastercard is seeking a Lead Data Scientist to lead and grow the data science team, delivering production-grade machine learning services across financial applications. The ideal candidate will be a strong technical leader, able to mentor the team, plan projects, and communicate findings to executives and clients. Responsibilities: - Lead, mentor and expand the data science team and tech stack focused on production-grade services and capabilities. - Plan and direct data science / machine learning projects within the team. - Design and implement machine learning models for financial applications including transaction classification, temporal analysis, risk modeling from structured and unstructured data. - Measure, validate, implement, monitor and improve performance of both internal and external facing models. - Apply ML methods (SVM, Random Forest, XGBoost, LightGBM, CATBoost, etc), deep learning (LSTM, RNN, Transformer) and LLMs to analytical problems. - Propose creative solutions to challenges new to the company and the financial industry; communicate findings clearly to business leaders and clients. - Leverage best practices in ML and data engineering to develop scalable solutions. - Identify resource gaps and provide sustainable solutions to benefit the team. - Communicate vision and roadmap effectively; strong writing and public speaking skills. Requirements: - Experience mentoring and leading data science teams. - Substantial experience in data science/model develo