Model Manager

LPL Financial•Fort Mill, SC

9d•$81,267 - $135,445

About The Position

The Model Manager is responsible for overseeing a portfolio of complex and strategically important AI-driven supervisory models, ensuring their performance, governance compliance, documentation accuracy, and overall lifecycle management. This role continuously monitors model effectiveness, analyzes trends and performance metrics, and proactively identifies and escalates risks while recommending data-driven remediation strategies. The Model Manager leads model enhancements and change initiatives, including parameter updates, logic refinements, threshold recalibrations, and data modifications, while ensuring adherence to established governance and change management frameworks. Working closely with technology, compliance, and business stakeholders, the role directs validation, quality control, testing, and deployment activities to maintain model accuracy, regulatory compliance, and operational effectiveness. Additionally, the Model Manager drives continuous improvement by identifying optimization opportunities, conducting root-cause analyses, and leveraging benchmarking, A/B testing, and scenario-based validation to support the evolution of next-generation supervisory models.

Requirements

5+ years of experience in financial services with meaningful exposure to supervision, compliance, model risk management, or regulatory operations.
Bachelor’s degree in Business, Finance, Data Analytics, or a related field required.
Demonstrated experience supporting or managing analytical or AI model lifecycles within a financial service or similarly regulated environment – including monitoring validation and change management activities.
Working knowledge of FINRA, SEC, and NASAA regulatory frameworks as they relate to supervisory obligations and first-line control environments.
Experience collaborating across compliance, technology, and operations functions, with the ability to translate technical model concepts for non-technical supervisory audiences.
Proficiency in Microsoft Word, Excel, PowerPoint, experience with data visualization tools, query languages (SQL), or model monitoring platforms strongly preferred.
Analytical and critical-thinking skills, with the ability to identify patterns in model performance data, diagnose root causes, and develop well-reasoned, actionable recommendations.
Written and verbal communication skills, with experience preparing concise, professional reports and presenting findings to management-level stakeholders.
Organized and detail-oriented, with the ability to manage multiple concurrent model workstreams with accuracy and professionalism.
A genuine intellectual curiosity about AI, machine learning, and the application of emerging technologies to regulatory and supervisory challenges.

Nice To Haves

FINRA Series 7; willingness and eligibility to obtain required upon hire.
Additional FINRA licensing (Series 24, 66)

Responsibilities

Own and manage a portfolio of complex, high-scale, or strategically significant supervisory AI models – taking full accountability for each model’s performance, governance compliance, documentation currency, and lifecycle status.
Conduct ongoing performance monitoring and trend analysis across assigned models, interpreting results against established thresholds and proactively escalating material deviations to the VP with root-cause analysis and recommended remediation.
Manage model changes – including parameter adjustments, logic refinements, threshold recalibrations, and data feed modifications – through the firm’s change management and governance protocols, ensuring complete documentation at each stage.
Lead validation and quality control exercises for assigned models, coordinating with technology and compliance stakeholders to ensure models remain accurate, regulatory-compliant, and operationally effective.
Proactively identify and prioritize enhancement opportunities across the assigned model portfolios, synthesizing performance data, supervisory feedback, and regulatory developments into well-reasoned improvement recommendations for VP review.
Design and lead model enhancement initiatives – from scoping and requirements development through testing, validation, and deployment – coordinating cross-functional resources and managing timelines with minimal VP oversight.
Conduct root-cause analysis when models produce anomalous, degraded, or inconsistent outputs, developing and executing remediation plans in a timely and thorough manner.
Support A/B testing, benchmarking, and scenario-based validation to inform model update decisions and next-generation design.