S&R Director of Supervision Analytics and Modeling

Federal Reserve System•Atlanta, GA

3d•Onsite

About The Position

As an employee of the Atlanta Fed, you will help support our mission of promoting the stability and efficiency of the U.S. economy and financial system. Your work will affect the economy of the Southeast, the United States, and the world. The work we do here is important, and how we do it is just as important as what we do. We live our values of integrity, excellence, and respect every day. We do the right thing, we do things right, and we treat people right. A career at the Federal Reserve Bank of Atlanta gives you the chance to do work that touches lives and helps communities prosper. The Bank believes in flexibility to balance the demands of work and life while also recognizing the necessity of connecting and collaborating with our colleagues in person. Onsite work is an essential function of this position, and you are required to be onsite according to the schedule set by your management. The Director of Supervision Analytics and Modeling directs a team of data scientists to develop and implement leading edge models and tools designed to achieve operational efficiencies and improve financial sector risk identification for supervision. The director maintains awareness of model risk management best practices and oversees the team as they develop and deploy advanced analytics, models, and tools that require technical feasibility assessments, prioritization, workload management, and quality control oversight. The director develops collaborative partnerships with Reserve Bank and System stakeholders and advances the use and understanding of emerging technology throughout the organization.

Requirements

Bachelor's degree in data science, computer science, mathematics, statistics, mathematical finance, operations research, economics or similar field focused on advanced quantitative modeling with a minimum of 10+ years of diverse professional work experience which may include 3+ years of supervisory responsibilities.
OR Masters or PhD Degree in economics, mathematical finance, data science, computer science, mathematics, statistics, operations research or similar field focused on advanced quantitative modeling with a minimum of 7 years of diverse professional work experience which may include 1+ year(s) of supervisory responsibilities.
Seven + years with Masters degree; Ten + years without
Knowledge Areas: Financial Economics, Bank Accounting, Data and General Architecture Principles, Maturity Models and Business Processes, Emerging Technologies, Strategic and Analytical skills, Quantitative skills, advanced degrees in STEM fields and knowledge of Statistical/econometric/AI/ML methods for developing models and tools
Technical Skills: General programming, Statistical analysis, Architecture Frameworks and Methodologies, Data Management Standards and Frameworks, Data Analytical & Business Intelligence Tools
Proficient with at least one widely used statistical or data science programming language (such as R or Python)
Managerial Competencies: Communicates Effectively / Displays Interpersonal Savvy, Demonstrates Decision Quality, Develops and Engages Talent, Exhibits Courage, Reflects a Strategic Mindset, Values Differences, Ensures Accountability

Nice To Haves

Financial Economics
Bank Accounting
Data and General Architecture Principles
Maturity Models and Business Processes
Emerging Technologies
Strategic and Analytical skills
Quantitative skills
advanced degrees in STEM fields and knowledge of Statistical/econometric/AI/ML methods for developing models and tools
General programming
Statistical analysis
Architecture Frameworks and Methodologies
Data Management Standards and Frameworks
Data Analytical & Business Intelligence Tools
Proficient with at least one widely used statistical or data science programming language (such as R or Python)
Communicates Effectively / Displays Interpersonal Savvy
Demonstrates Decision Quality
Develops and Engages Talent
Exhibits Courage
Reflects a Strategic Mindset
Values Differences
Ensures Accountability

Responsibilities

Lead and develop a high-performing team of data scientists across diverse skill levels and specializations, providing direct supervision, strategic direction, and performance management while fostering a culture of accountability, collaboration, and measured risk-taking.
Responsible for end-to-end talent management including hiring, goal-setting, assignment delegation, feedback delivery, and career development with a focus on inclusive leadership practices.
Drive the strategic development and deployment of artificial intelligence, machine learning, and augmented intelligence solutions to achieve operational efficiencies, improve financial sector risk identification, and influence System-wide analytic tools and models.
Lead innovation and continuous improvement initiatives through forward-looking internal processes that enhance risk monitoring, reporting, and supervisory responsiveness across the organization.
Exercise strong business judgment in resource allocation and strategic prioritization, balancing competing demands across local Reserve Bank and Federal Reserve System priorities while ensuring efficient utilization of departmental assets.
Coordinate cross-functional resources and influence outcomes of analytic tools, risk identification frameworks, and operational models at both local and System levels.
Serve as the critical bridge between technical execution and business strategy by collaborating with multiple business lines, FRS experts, and regulatory stakeholders to translate complex requirements into actionable features and deliverables.
Establish strong relationship management practices and effective communication channels to deliver high-quality products and models that meet diverse stakeholder needs.
Provide recognized technical expertise and thought leadership on artificial intelligence, machine learning, and specialized analytics topics through research, internal consultation, and external engagement at conferences and training venues.
Maintain current knowledge of model risk management best practices, technical modeling methodologies, and data management standards while influencing strategic initiatives across System, Reserve Bank, and line of business levels.