Data Scientist, Fraud

This is a fully remote position, open to applicants in India.

📋 Description

• Develop, assess, and aid in the production of machine learning models aimed at fraud detection.

• Manage continuous monitoring and retraining cycles for models.

• Design and conduct experiments to evaluate the impact of fraud interventions while balancing customer satisfaction and loss mitigation.

• Analyze fraud typologies across various product lines to guide prioritization and investment strategies.

• Create and sustain anomaly detection systems to identify emerging fraud vectors before they escalate.

• Collaborate with fraud operations, engineering teams, product managers, and data analysts.

• Interpret model results into actionable fraud prevention strategies.

• Produce high-quality ML models and anomaly detection systems for production use.

• Assist in product prioritization and strategic investment decisions through thorough fraud typology analysis.

⛳️ Requirements

• A solid background in statistics, with a degree in a quantitative discipline (Statistics, Mathematics, Engineering, Computer Science, or equivalent).

• At least 3 years of experience in data science, decision science, or risk analytics related to fraud, payments, or financial crime.

• Practical experience in building and deploying machine learning models within a production setting.

• Experience in fraud, risk, or financial services is highly advantageous.

• Strong foundation in experimentation, statistical inference, model evaluation, and feature engineering.

• Ability to thrive in fast-paced, cross-functional teams with significant ownership responsibilities.

• Proficiency in Python and SQL.

• Comfortable navigating the entire model development lifecycle.

• Capable of clearly communicating technical insights to non-technical stakeholders and translating findings into actionable steps.

🏝️ Benefits

• An environment focused on learning and development.

• Opportunities for knowledge sharing, training, and regular internal technical discussions.

• Competitive salary.

• Pension plan.

• Comprehensive health insurance.

• Annual bonus.

• Additional benefits.

• Prioritization of employee well-being.

• Commitment to an equal opportunity and inclusive workplace.