Lead Fraud Analytics Implementation Manager

Skill; Sioux Falls (SD); 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 financial ecosystem. You will bridge the gap between data science and business execution, evaluating 4-5+ models, navigating MRM governance, and leading deployment into the fraud framework.

This role requires strong ML, project management, and risk governance experience, with responsibilities across cross-functional teams and external partners

Qualifications

• Hands-on experience with ML algorithms, metrics (ROC-AUC, PR), and feature evaluation.
• Experience leading complex deployments and cross-functional teams.
• Familiarity with Model Risk Management (MRM) governance and approvals.
• Strong data analysis of large transaction and risk datasets.

Responsibilities

• Benchmark, evaluate, and test fraud models for ROI.
• Lead end-to-end model deployment within fraud ecosystem.
• Coordinate with MRM for validation and approvals.
• Manage cross-functional data science and infra roadmaps.
• Align stakeholders between data science, partners (e.g., Mastercard), and risk executives.

Skills

Data Science

Project Management

MRM

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.

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