Data Scientist Associate/I/II

Federal Reserve Bank of Philadelphia•Cleveland, OH

6d•Onsite

About The Position

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. As part of the nation’s central bank, we are a team of mission-driven professionals who are committed to serving with excellence. Our core values are: Build Trust Every Day, Own Our Actions, Collaborate with Intention, and Be Open to Change. For twenty-six years in a row, we’ve been named “One of Northeast Ohio’s Best Places to Work” by North Coast 99. This prestigious award honors organizations with outstanding employment practices, including compensation, benefits, training, recruitment, retention, community services, and employee communications. Follow us on LinkedIn, Twitter, Instagram, and our YouTube channel – Cleveland Fed BETR, Bank Exams Tailored to Risk, is the Federal Reserve’s risk-focused approach to community and regional bank supervision. The BETR Metrics Office, located at the Federal Reserve Bank of Cleveland, develops and maintains forward-looking models to help define the scope of exams at hundreds of supervised firms across the country – combining quantitative insight with examiner judgment. We conduct rigorous, data-driven assessments of banking sector and financial market conditions; promote more efficient, effective supervisory processes; and drive actionable research to support supervision and policy more broadly. As part of the Federal Reserve’s mission, we care about ensuring a safe, sound, and fair financial system. Come join us!

Requirements

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, GitLab, DVC, MLFlow, etc.
Communication skills - both written and verbal.
Ability to deliver presentations and co-author publication-quality reports

Nice To Haves

Experience in data mining, artificial intelligence, agentic frameworks, machine learning, and/or other emerging fields is a plus.

Responsibilities

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.