Data Science Manager (Fraud)

Accountabilities:

• Define the strategy and roadmap for fraud detection and prevention, staying ahead of emerging fraud typologies.
• Lead and mentor a team of data scientists, fostering continuous learning and technical mastery.
• Design, build, and deploy real-time fraud scoring models and decision logic using high-volume transactional, behavioral, and device data.
• Optimize model outcomes by balancing precision, recall, and user experience through experimentation and iterative improvement.
• Ensure data quality, model governance, and compliance with regulatory requirements.
• Monitor model performance continuously, identifying drift and emerging risks to maintain robust fraud defenses.
• Collaborate with product and engineering teams to integrate fraud prevention seamlessly into business workflows.
Requirements:
• 6+ years of experience in data science, decision science, or fraud risk, preferably within financial services.
• Degree or equivalent experience in a quantitative field (Statistics, Mathematics, Engineering, or similar).
• Deep understanding of fraud typologies, financial crime, and regulatory environments.
• Proficiency in SQL and Python for data analysis and model development.
• Demonstrated success building and deploying machine learning models at scale.
• Experience leading or mentoring technical teams in fast-paced, high-impact settings.
• Strong analytical, problem-solving, and communication skills to translate complex insights into actionable recommendations.
• Preferred: Experience with A/B testing, user retention/churn modeling, and advanced degrees (MSc or PhD).
Benefits:
• Competitive salary and performance-based incentives.
• Fully remote work with flexible hours.
• Health insurance, pension, and other comprehensive benefits.
• Opportunities for professional growth and continuous learning in advanced analytics and fraud management.
• Collaborative, inclusive, and human-centric work culture that values mastery and innovation.