Lead Fraud Analytics Implementation Manager

Aquent; Irving (TX); Hybrid; USD 150,000 - 190,000; Full time

Job summary

Aquent seeks a Lead Fraud Analytics Implementation Manager to evaluate, optimize, and deploy fraud detection models within our enterprise fraud ecosystem. You will bridge data science with business execution, directing 4–5+ models through governance and regulatory approvals.

Hybrid role with on-site collaboration across multiple locations; you will coordinate with MRM, fraud operations, and external partners to ensure robust, compliant deployment and ongoing optimization.

Qualifications

• Data science background with ML model evaluation experience.
• Experience leading complex, multi-stakeholder technical deployments.
• Direct experience navigating Model Risk Management (MRM) in financial services.
• Ability to analyze large transaction and risk datasets to drive decisions.

Responsibilities

• Benchmark, evaluate, and test fraud models; assess business impact and ROI.
• Lead end-to-end model deployment and governance with MRM teams.
• Direct cross-functional roadmaps combining data science, infrastructure, and rules updates.
• Integrate model scores into active fraud rules and detection pathways.
• Coordinate with data science teams, external partners (e.g., Mastercard), and risk executives.

Skills

Data science

Project management

Model risk management

Analytics experience

Job description

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.