Visa - Highlands Ranch, CO
posted 5 months ago
Visa U.S.A. Inc., a Visa Inc. company, is seeking a Sr. Data Scientist to join our team in Highlands Ranch, CO. This role is pivotal in providing technical leadership within a team that generates business insights from big data. The successful candidate will be responsible for identifying actionable recommendations and effectively communicating findings to clients. The position requires innovative thinking to leverage our unique data to address various business challenges. The Sr. Data Scientist will engage with clients to understand their challenges and utilize data to provide convincing insights. In this role, you will extract and analyze data to form informed opinions on how to best assist our clients, developing visualizations that make complex analyses accessible to a broad audience. You will also identify opportunities to create products from analyses that can serve multiple clients. Collaboration with stakeholders across the organization is essential to leverage Visa data for driving business solutions. The role involves mining and analyzing data from company databases to optimize product offerings, marketing techniques, and business strategies for Visa and its clients. Additionally, you will assess the effectiveness and accuracy of new data sources and data gathering techniques, develop custom data models and algorithms, and apply predictive modeling to enhance customer experiences and business outcomes. The position requires synthesizing ideas and proposals in writing and engaging in productive discussions with both external and internal stakeholders. You will provide guidance on modern analytic techniques and business applications to unlock the value of Visa's unique data set, aligning with market trends and client needs. Managing multiple data science projects with diverse cross-functional stakeholders will also be a key responsibility. This position reports to the Highlands Ranch, Colorado office and may allow for partial telecommuting, with a hybrid work model expected.