Serves as subject matter expert in designing and implementing data governance frameworks for key reporting and business processes
Partners with business leaders to ensure data quality and timely delivery
Performs independent challenge and validation of complex risk models
Supports special projects as needed
Identifies, documents, and assesses model data inputs to evaluate completeness, consistency, lineage, and effectiveness of controls
Defines, documents, and executes data validation requirements for model validation activities
Performs independent validation of statistical, machine learning, and Generative AI models
Develops and executes benchmarks, challenger models, and replication analyses
Assesses overall model health and compliance with data and model risk management policies
Partners with model owners, developers, and business stakeholders to understand model design and business context

Requirements

Bachelor’s Degree or equivalent work experience – Required
Master’s Degree or higher in Statistics, Mathematics, Engineering, Computer Science, Economics, or a related field – Preferred
7+ years of experience working with statistical models, developing, and/or validating machine learning or Generative AI solutions, including rigorous testing and documentation – Required
Quantitative or analytical professional background in Finance, Economics, Statistics, Mathematics, Engineering, or a related discipline – Required
Experience in the financial services industry with exposure to data management, risk modeling, and regulatory compliance (e.g., BCBS 239, SR 11-7) – Required
Prior consulting, advisory, or second-line oversight experience within data governance, model validation, or risk analytics environments – Preferred
Knowledge of machine learning models, including development, validation, performance testing, and monitoring techniques
Generative AI expertise, including evaluation of LLM architectures, design decisions, implementation approaches, and associated risks such as hallucinations, bias, and toxicity
Hands-on experience with Retrieval-Augmented Generation (RAG) systems, including document ingestion, chunking strategies, embeddings, retrieval techniques, orchestration frameworks, and response grounding
Hands-on experience with AWS-native services such as Bedrock, Lambda, API Gateway, S3, IAM, CloudWatch, and MLOps or LLMOps capabilities
Strong Python proficiency for data manipulation, validation, automation of controls, and development of reproducible, well-documented scripts using version control
SQL proficiency, including data extraction, joins, aggregation, reconciliation, and validation across multiple data sources
Experience using Power BI to develop dashboards that visualize data quality metrics, trends, and validation outcomes.
Proven ability to communicate complex technical, data, and model risk concepts clearly to non-technical governance and risk stakeholders.
Strong analytical and critical thinking skills with the ability to independently challenge model assumptions and conclusions.
Effective written and verbal communication skills, particularly in documenting and explaining complex technical and governance topics.
Collaborative mindset with the ability to partner effectively with technical teams and business stakeholders.
High attention to detail and a strong commitment to quality, accuracy, and governance standards.

Benefits

Competitive rewards package
Health insurance
Paid time off
Flexible working arrangements
Professional development opportunities

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data governance frameworksstatistical modelsmachine learningGenerative AIdata validationquantitative analysisPythonSQLPower BIrisk modeling

Soft Skills

analytical thinkingcritical thinkingcommunication skillscollaborationattention to detailindependent validationproblem-solvingdocumentationstakeholder engagementquality assurance