Lead Data Scientist - Fraud Prevention

Requirements:

• Proven track record of deploying models from scratch, including data preprocessing, feature engineering, model selection, evaluation, and monitoring.
• Strong Python knowledge. Ability to read through code, especially Java. Demonstrable experience collaborating with engineering on services.
• Experience with statistical analysis and good presentation skills to drive insight into action.
• A strong product mindset with the ability to work independently in a cross-functional and cross-team environment.
• Good communication skills and ability to get the point across to non-technical individuals.
• Strong problem-solving skills with the ability to help refine problem statements and figure out how to solve them.

Nice to Haves:

• Experience in working with non-supervised algorithms.
• Prior experience in the fraud domain and a strong understanding of fraud detection techniques.

What You'll Be Doing:

• Model Maintenance and Improvement: Maintain and optimize existing risk models to ensure their accuracy and reliability. Continuously monitor model performance and implement improvements based on feedback and testing.
• Innovate and Develop: Lead the development and deployment of machine learning models, features and help deploy intelligence to production.
• Data Analysis & Intelligence Creation: Conduct thorough data analysis to identify trends, patterns, and anomalies that can aid in risk mitigation. Develop actionable intelligence and insights to inform the Fraud Risk Team's strategies.
• Collaboration & Communication: Work closely with the Fraud Risk Team to understand business processes and risk factors. Communicate complex data findings and insights effectively to non-technical stakeholders.
• Risk Reduction Initiatives: Identify opportunities to reduce the impact of risks on good customers through data-driven strategies and interventions. Develop and test strategies to balance risk mitigation with customer satisfaction.
• Documentation & Reporting: Document the development and maintenance processes for models and features. Prepare and present detailed reports and dashboards that reflect risk assessment outcomes and model performance.

Perks and Benefits:

• Not specified in the provided job description.