Hybrid Fraud Analytics Implementation Lead

Skill; Jacksonville (FL); Hybrid; USD 90,000 - 110,000; Part time

Job summary

Skill is seeking a Lead Fraud Analytics Implementation Manager to evaluate, optimize, and deploy advanced fraud detection models within our enterprise fraud ecosystem. You will bridge data science and business execution, evaluating 4-5+ models from internal and external sources and guiding their deployment.

You will lead governance with Model Risk Management, drive model validation, and coordinate cross-functional teams across data science, risk, and payments to integrate models into live rules

Qualifications

• Proven hands-on understanding of machine learning models and performance metrics.
• Experience leading complex multi-stakeholder technical deployments.
• Direct experience navigating MRM compliance in financial services.
• Comfort analyzing large transaction and risk datasets to drive decisions.

Responsibilities

• Model Evaluation & Optimization: Benchmark, evaluate, and test existing and proposed fraud models; assess performance, impact, and ROI.
• Implementation & Governance: Lead end-to-end model deployment; drive validation, documentation, and approvals with MRM.
• Technical Project Management: Align data science, infra requirements, and rules engine updates for integration.
• Rules & Detection Strategy: Integrate model scores into active fraud rules and payment workflows.
• Stakeholder Alignment: Coordinate between data science teams, external partners, MRM, and risk executives.

Skills

Data science background

Project & program management

Model Risk Management

Analytics experience

Job description

Skill is seeking a Lead Fraud Analytics Implementation Manager to evaluate, optimize, and deploy advanced fraud detection models within our enterprise fraud ecosystem. You will bridge data science and business execution, evaluating 4-5+ models from internal and external sources and guiding their deployment.

You will lead governance with Model Risk Management, drive model validation, and coordinate cross-functional teams across data science, risk, and payments to integrate models into live rules