We are
We are a highly profitable, fast-growing financial technology company powering the future of financial services. Our comprehensive suite of innovative and scalable embedded payments, cards, and lending products deliver financial services for millions of businesses and consumers around the globe. We are backed by leading investors and serve the world’s most essential fintech and technology companies. Together with its partners, we are reshaping global finance and financial inclusion.
We are on a mission to build the infrastructure that propels access, inclusion, and the democratization of financial services.
You are
We are the Data Science team, focused on regulatory compliance. Our work primarily focuses on Fair Lending and Anti Money Laundering by assisting the relevant teams with detailed analysis and/or machine learning models. Our team is working with vital analytics to ensure we are within the guidelines for banking regulations. Our team uses SAS, Python and various machine learning algorithms to provide on-demand analysis.
You are detail oriented with an analytical, logical way of thinking, and are passionate about making data driven decisions.
This is an open role for a new member of the AML Data Science team responsible for performing analysis on data focused on Anti-Money Laundering (AML/CFT), interpreting results and developing actionable insights to accelerate operational excellence and demonstrate compliance.
You will
Present data and analysis in a clear and concise manner allowing the audience to quickly understand the results and recommendations so they can make informed data driven decisions.
Collaborate with various partners to provide a holistic view of the analysis. Measure and monitor results of applied recommendations and present adjustments.
Develop scenarios involving tuning, calibration, segmentation and optimization.
Provide routine and ad hoc reporting tied to AML/CFT compliance.
Develop insightful and compelling summary reporting and dashboards for management and business line partners.
Support the development of policies and procedures for AML transaction monitoring life cycle, including reviews of scenario validation, segmentation and optimization tools.
