Drive the roadmap for internal AI & ML initiatives, aligning with business objectives and regulatory requirements.
Serve as a subject matter expert on fraud analytics, advising leadership on emerging threats and mitigation strategies.
Architect and deploy cutting-edge machine learning and AI models to maximize fraud recovery, behavioral analysis, and predictive fraud prevention.
Optimize models for scalability, real-time performance, and minimal false positives.
Identify new data sources and develop advanced feature engineering techniques to enhance fraud detection capabilities.
Lead research into novel algorithms, including graph analytics and deep learning approaches.
Partner with Legal, Leadership, and Engineering teams to integrate solutions into enterprise systems.
Communicate complex technical concepts to non-technical stakeholders and influence decision-making.
Guide and mentor junior data scientists, fostering a culture of innovation and continuous learning.
Ensure models adhere to regulatory standards (OCC, PCI DSS) and ethical AI practices.

Requirements

7+ years of experience in data science, with at least 3 years focused on fraud detection or financial risk analytics.
Expertise in machine learning frameworks (scikit-learn, TensorFlow, PyTorch) and advanced statistical modeling.
Strong proficiency in Python, SQL, and Snowflake.
Deep understanding of financial systems, payment networks, and fraud typologies.
Experience deploying models in production environments and working with real-time streaming platforms.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningAI modelsfraud detectionfeature engineeringgraph analyticsdeep learningstatistical modelingPythonSQLSnowflake

Soft Skills

communicationleadershipmentoringinfluencinginnovationcollaborationproblem-solvingdecision-makingorganizational skillscontinuous learning