Senior Data Scientist, Mercury

• Have 4+ years of experience working with and analyzing large datasets to solve problems and drive impact, with 2+ years of experience working in compliance or fraud
• Have fluency in SQL and experience using it creatively with imperfect data
• Have consistent experience with developing dashboards using data visualization tools
• Have the ability to proactively ask questions, turn them into analyses, and make your case to various stakeholders, including senior leadership
• Be super organized and communicative. You will need to prioritize and manage projects to maximize impact, supporting multiple stakeholders with varying quantitative skill levels
• Be comfortable with decision making in a fast-paced, ambiguous environment, while demonstrating curiosity and a growth mindset

Desirable

• We are looking to hire a data scientist who will advance our data-first approach to managing risk by surfacing key metrics, building automated and reliable reporting to cross-functional teams, diving into complex data questions, and working to enhance our data infrastructure and understanding from end-to-end
• In this role, you’ll be responsible for proactively deriving data insights and partnering with risk, compliance, engineering, product, and design to inform how we understand and mitigate financial, regulatory, and reputational risk
• You’ll build a data-informed culture across Mercury so that we can all determine what’s happening, react quickly, and invest intelligently
• You will work on projects end-to-end and build deep domain expertise in the intersection of Data Science and Risk
• Own and manage core risk business and operational metrics and data, becoming the company-wide expert on the topic
• Build observability for our internal operations and risk controls efficacy through analytical deep dives and key metric dashboards
• Respond to ad hoc data requests from within Risk and from cross-functional partners in Audit, Compliance, and Product
• Partner with Risk stakeholders and cross-functional teams to identify impactful business questions, conduct deep-dive analysis, translate data insights into actionable recommendations and communicate findings to audiences at all levels to inform data-driven decisions
• Analyze historical data to identify trends, patterns, and risk factors, informing the design of risk mitigation strategies
• Collaborate with other Data Scientists and Data Engineers to build and improve data pipelines, tools, and infrastructure to streamline data collection, processing, and analysis workflows, and ensure the integrity, reliability, and security of data assets
• Leverage data models and advanced analytics techniques to design long-term solutions including enhancements of existing strategies and building new process improvements