Model Governance Compliance Analyst

AKUVO LLC•Malvern, PA

About The Position

AKUVO is seeking an experienced financial-services model governance and compliance professional to own and administer the governance framework supporting our proprietary predictive scores, model attributes, and related analytical capabilities. You will help ensure that AKUVO’s models are appropriately documented, reviewed, validated, monitored, and explainable to customers, auditors, and regulatory examiners. You will work closely with Machine Learning, Data Engineering, Product, Legal, Compliance, and financial-institution subject-matter experts throughout the model-development lifecycle. The ideal candidate combines financial services regulatory knowledge with sufficient technical and analytical fluency to understand how models are developed, how scores and attributes are produced, how data moves through the model lifecycle, and how model outputs are used within customer workflows. This role also supports governance of AI capabilities where they rely on predictive scores, model attributes, analytical outputs, or other material decision-support components.

Requirements

3+ years in model risk management, model governance, model validation, or financial-services compliance, with 2+ years working directly with predictive models, credit-risk models, or scoring systems.
Experience at a financial institution, regulatory agency, model-risk consulting firm, or financial-services technology company in a model governance, validation, or compliance capacity.
Working knowledge of the model development lifecycle — feature engineering, training, validation, monitoring, and deployment — sufficient to review technical work and identify risks without owning model development directly.
Familiarity with fair-lending requirements and consumer-protection regulations applicable to models, scores, and decisioning, including ECOA and Regulation B, FCRA, UDAAP, adverse-action explainability, and NCUA or federal banking agency expectations.
Experience developing or applying model governance frameworks, model inventories, validation standards, documentation requirements, monitoring programs, or change-control processes.
Experience supporting audits, regulatory examinations, customer due diligence, or model-risk reviews, and translating technical model information for nontechnical audiences.
Strong analytical and critical-thinking skills; ability to evaluate model performance results, segment analyses, score distributions, and monitoring evidence and identify risks or concerns.
Strong written communication and documentation skills; ability to work across Machine Learning, Data Engineering, Product, Legal, Compliance, and executive stakeholders.
Active use of AI-assisted tools to improve productivity, documentation quality, and analytical work.

Nice To Haves

Experience applying financial services model-risk frameworks, with familiarity adapting governance practices to credit union and community-bank environments.
Familiarity with applicable FFIEC guidance; federal banking agency and NCUA supervisory expectations; and fair-lending requirements related to model risk management, disparate-impact analysis, and third-party model oversight.
Experience with fair-lending testing, disparate impact analysis, or adverse-action explainability for model-based decisions.
Experience with collections, delinquency, credit risk, propensity, or behavioral scoring models in a lending or servicing context.
Experience supporting NCUA, CFPB, OCC, FDIC, or state regulatory examinations involving models, scores, or algorithmic decisioning.
Familiarity with AI and machine-learning governance frameworks, responsible-AI expectations, or emerging model-risk guidance covering AI and automated decisioning.
Experience with model-related data governance, including lineage, feature and attribute definitions, training-data documentation, and input-output controls.
Experience in a B2B SaaS or financial-technology environment serving multiple financial-institution customers.

Responsibilities

Develop and maintain AKUVO’s governance framework for predictive models, scores, and model attributes, including a comprehensive model inventory covering ownership, intended use, methodology, inputs, outputs, dependencies, limitations, monitoring requirements, approvals, and status; establish governance requirements proportionate to each model’s complexity, customer impact, and risk.
Participate throughout AKUVO’s Model Development Framework to ensure documentation, validation, monitoring, compliance, and approval requirements are incorporated from Discovery & Design through Deployment & Monitoring.
Review model methodology, training and testing data, assumptions, features, attributes, performance results, explainability, limitations, intended use, and implementation controls; coordinate independent validation and effective challenge without duplicating model-development responsibilities.
Establish standards for model documentation, model cards, attribute definitions, validation packages, monitoring reports, change records, and customer-facing materials; maintain governance decisions, findings, exceptions, remediation commitments, approvals, and supporting evidence.
Review model attributes and features for relevance, reliability, explainability, potential bias, prohibited or sensitive characteristics, and unintended proxy effects.
Coordinate and support fair-lending and consumer-impact reviews in partnership with Legal, Compliance, Machine Learning, and financial-institution subject-matter experts; assess model design, attributes, outputs, and intended use for applicable legal and consumer-impact considerations, including ECOA and Regulation B, FCRA, fair-lending requirements, adverse-action explainability where applicable, UDAAP, privacy, and relevant NCUA or federal banking agency expectations.
Define requirements for model monitoring — performance, calibration, stability, drift, score distributions, attribute behavior, usage patterns, and operational outcomes — and review material changes to methodology, attributes, data sources, thresholds, or intended use to determine required testing, validation, documentation, and approval.
Partner with Data Engineering to document model-related data lineage, transformations, feature creation, source-system dependencies, and training-to-production consistency.
Support customer due-diligence requests, model-risk reviews, audits, and regulatory examinations related to AKUVO’s scores, attributes, and model-development practices; translate technical model information into clear explanations for customers, auditors, examiners, executives, and nontechnical stakeholders.
Monitor NCUA, CFPB, federal banking agency, fair-lending, model-risk, consumer-protection, and privacy guidance and assess its impact on AKUVO’s models and customers; support governance of third-party models and external data sources used in the model-development or scoring lifecycle; support AI governance where an AI capability materially relies on AKUVO scores, model attributes, or model-based recommendations.
Use AI-assisted workflows and internal agents to accelerate documentation review, evidence collection, control mapping, monitoring analysis, and customer or examination responses while maintaining human accountability.