Lead Data Science Specialist, Remote
GuideWell Source
PythonSQL
Lead Business Analyst, Fraud
Happy Money
full-time
Posted on:
Origin: • 🇺🇸 United States
Visit company websiteSalary
💰 $135,000 - $155,000 per year
Job Level
Senior
Tech Stack
PythonSQL
About the role
- Develop and implement fraud strategies leveraging data, machine learning models and advanced analytics tools to mitigate fraud losses and maintain superior customer experience.
- Manage and optimize existing strategies effectively controlling risks due to first party and third-party fraud.
- Design A/B tests and understand the risk and customer experience trade-offs.
- Lead fraud forecasting and budgeting activities by modeling fraud losses, operational impacts, and fraud trend assumptions.
- Lead the efforts to constantly iterate and improve existing strategies while identifying additional automation opportunities.
- Continue to innovate by testing new data, analytics approaches and models.
- Monitor and analyze portfolio performance at a granular segment level on an ongoing basis. Identify trends and conduct root-cause analysis to isolate key performance drivers.
- Communicate findings and recommendations to the Risk Team  and the broader Happy Money community.
- Identify and evaluate new vendors/data sources and create business cases and present them to senior management.
- Partner with Product, Marketing, Engineering and external vendor teams to implement strategies and monitor the strategy performance.
- Bring innovative/out of the box thinking to solve problems and make recommendations.
- Conduct ad-hoc analysis related to risk management, product, operations.
- Drive feedback loops using member complaints, bureau data, and operations insights to improve fraud detection performance.
- Provide subject matter expertise during fraud incidents, investigations, and root cause analyses.
Requirements
- 4+ years of experience in business analytics and/or data science
- 2+ years of experience in quantitative analytics, with a strong preference for credit risk or fraud risk
- Bachelor’s or Master’s degree in Business, Statistics, Mathematics, Computer Science, Engineering or Economics
- Strong analytical and problem-solving skills: deep experience working with data-driven business strategies and rule/policy making, with a strong preference toward those doing so in identity fraud.
- Strong proficiency with Python & SQL and the ability to analyze large, complex datasets and derive actionable insights
- Familiarity with machine learning models or techniques
- Excellent problem-solving skills with the ability to synthesize complex data into actionable insights for both technical and non-technical audiences
