Data Scientist Associate​/I​/II

Position Overview

The Federal Reserve Bank of Cleveland’s mission is to foster the stability, integrity, and efficiency of the nation’s monetary, financial, and payment systems while representing the Fourth District perspectives. This position can be located at our Cleveland, OH, Cincinnati, OH, or Pittsburgh, PA offices.

• Proven capability in designing, developing, and conveying the results of innovative models (including econometric, predictive, behavioral, simulation, or other) to ensure identification of firm-specific, sector, and broader banking industry trends and risk developments in an informative, forward‑looking, and critically focused manner. Experience in data mining, artificial intelligence, agentic frameworks, machine learning, and/or other emerging fields is a plus.
• Proven capability to contribute to large‑scale analytic projects through all stages of development, including formulating concepts; developing and executing analysis plans; implementing and deploying models and tools; and reporting and presenting final research results.
• Proven ability in scalable data processing (e.g., distributed computing, cloud platforms, data warehousing) and MLOps/deployment processes (e.g., model serving, containerization, model monitoring).
• Beginning to show capability to lead transformation of large and complex datasets in a manner that is suitable for analysis.
• Visualizes and reports analytic findings using a variety of methods to promote understanding of key insights and actionable takeaways for both technical and non‑technical audiences.
• Delivers formal presentations and creates analytical reports and executive summaries of findings.
• Mentors junior staff on skills and knowledge areas needed for all elements of data science product development.
• Performs other duties as assigned or requested. Adheres to the Bank's attendance policies through regular and prompt attendance.

• Experienced in application of advanced quantitative techniques, including econometrics, machine learning/artificial intelligence, natural language processing, scenario modeling, etc.
• Knowledge of banking and financial markets, their regulation, and how to use quantitative methods to identify material risks at financial institutions (e.g., credit, capital, liquidity, or other risks) and evaluate impacts of supervisory policy.
• Knowledge of data science tools and compute platforms (e.g., Red Hat Enterprise Linux, Databricks, AWS, etc.)
• Knowledge of effective model risk management and data governance practices
• Ability to perform independent research with limited direction.

• Experience with statistical programming using Python and/or R.
• Experience working with large data sets and data processing tools like SQL, Hadoop, Spark, etc.
• Experience with data visualization tools, preferably Plotly Dash or RShiny.
• Experience with code, data, and model management tools like Git, Git Lab, DVC, MLFlow, etc.
• Communication skills – both written and verbal. Ability to deliver presentations and co‑author publication‑quality reports.

• Bachelor's degree in data science, computer science, mathematics, statistics, mathematical finance, operations research, economics or similar field focused on advanced quantitative modeling with 7+ years of related work experience
• OR Preferred – Master’s or PhD degree in economics, mathematical finance, data science, computer science, mathematics, statistics, operations research or similar field focused on advanced quantitative modeling with 5+ years of related work experience.

• Bachelor's degree in data science, computer science, mathematics, statistics, mathematical finance, operations research, economics or similar field focused on advanced quantitative modeling with 5+ years of related work experience
• OR Preferred – Master’s or PhD degree in economics, mathematical finance, data science, computer science, mathematics, statistics, operations research or similar field focused on advanced quantitative modeling with 3+ years of related work experience.