Lead Fraud Analytics Implementation Manager

Skill; Dallas (TX); Hybrid; USD 90,000 - 110,000; Full time

Job summary

Skill is seeking a Lead Fraud Analytics Implementation Manager for a ~12-month contract with a hybrid work model (3 days on-site). Location spans Dallas/Irving, TX and other U.S. cities. The role combines data science, governance, and project leadership to deploy advanced fraud detection models into our enterprise fraud framework.

The ideal candidate will evaluate 4-5+ ML models, navigate MRM processes, and drive cross-functional deployment with risk executives and external partners.

Qualifications

• Data science background with ML algorithms and model performance metrics (ROC-AUC, Precision/Recall).
• Experience leading complex, multi-stakeholder deployments and model integration lifecycles.
• Direct experience navigating Model Risk Management (MRM) governance and approvals in financial services.
• Ability to analyze large transaction/risk datasets to drive decisions.

Responsibilities

• Benchmark, evaluate, and test fraud models; assess business value and ROI.
• Lead end-to-end deployment into the fraud ecosystem with MRM approvals.
• Direct cross-functional roadmaps combining data science and infrastructure requirements.
• Collaborate with fraud operations to integrate model scores into rules and payment pathways.
• Act as liaison between data science, external partners (e.g., Mastercard), MRM, and risk executives.

Skills

Data science

Project management

MRM compliance

Analytics

Job description

Overview

• Placement Type: Temporary
• Salary: $65-80 Hourly
• Salary: $75 - 78 / hourly as W2
• Start Date: Oct 26, 2026

Lead Fraud Analytics Implementation Manager

Engagement Duration: ~12-Month Contract

Location Policy: Hybrid (3 days/week on-site)

Locations: Dallas/Irving, TX | New Castle, DE | Jacksonville, FL | Sioux Falls, SD

Position Overview

We are seeking a Lead Fraud Analytics Implementation Manager with a strong data science and project management background to evaluate, optimize, and deploy advanced fraud detection models within our enterprise financial ecosystem.

In this role, you will bridge the gap between complex data science and business execution. You will independently evaluate the performance of 4-5+ machine learning models (including external vendor/Mastercard solutions), determine their business value, navigate the Model Risk Management (MRM) governance and approval process, and lead the technical deployment into our fraud detection framework.

Key Responsibilities

• Model Evaluation & Optimization: Benchmark, evaluate, and test existing and newly proposed fraud models; assess model performance, business impact, and ROI to drive prioritization.
• Implementation & Governance: Lead end-to-end model deployment into the fraud ecosystem. Partner closely with Model Risk Management (MRM) teams to drive model validation, documentation, and formal regulatory/internal approvals.
• Technical Project Management: Direct cross-functional roadmaps combining data science execution, infrastructure requirements, and rules engine updates to ensure smooth model integration.
• Rules & Detection Strategy: Collaborate with fraud operations and rule-building teams to integrate model scores into active fraud rules and payment execution pathways.
• Stakeholder Alignment: Serve as the central point of contact between data science teams, external technology partners (e.g., Mastercard), MRM, and business risk executives.

Required Qualifications & Skills

• Data Science Background: Proven hands-on understanding of machine learning algorithms, model performance metrics (ROC-AUC, Precision/Recall, population stability index), and feature evaluation.
• Project & Program Management: Demonstrated experience leading complex, multi-stakeholder technical deployments and model integration lifecycles within large organizations.
• Model Risk Management (MRM): direct experience navigating MRM compliance, governance frameworks, model validation, and approval processes in financial services.
• Analytics Experience: Deep comfort analyzing large transaction and risk datasets to drive operational decision-making.

Preferred Qualifications

• Domain Expertise: Prior experience in fraud analytics, fraud rule development, payment transaction flows, or card issuer risk management.
• Ecosystem Familiarity: Experience working with network mandates, issuer-side implementations, or Mastercard enterprise fraud solutions.
• Financial Services: Experience within large-scale financial institutions or complex banking environments.

#LI-MG1