The Sr Data Scientist, Risk will offer a strategic perspective, deep analytical and modeling capabilities, and a collaborative working style. The right candidate will have strong intellectual curiosity and passion for achieving business results
An ability to quickly define the problem, research and leverage state-of-the-art modeling techniques, and provide timely recommendations will be essential. Key skills will include a strong analytical mindset, deep understanding of most popular machine learning algorithms and lead key initiatives with integrity and a passion for investigations, problem solving, and troubleshooting
Identify complex fraud patterns and their technical root causes through detailed data mining and analysis, including identification of sophisticated fraud methods employed by actors who are deliberately trying to avoid detection
Serve as technical SME by sharing new data mining techniques, maintaining technical reference documentation, and interfacing with partner technology teams
Collaborate across business and technology stakeholders to communicate analytical findings to both technical and non-technical audiences
Provide technical guidance for engineering projects that incorporate new data points into the investigation team’s toolkit, such as API integrations or internal data transformations
Link Analysis/Graph analytics to find and mitigate deeply-connected fraud networks and detect new accounts being added to these networks
Unsupervised learning methods to augment existing supervised models, or detect portfolio anomalies
Development of machine learning models
Partner with product and engineering team in implementing features and models, and enhancing systems