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Sr. Data Scientist, Research Data & Technology - FS - R024402

Primary Location : DC-Washington

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Employee Status : Regular

Overtime Status : Exempt

Job Type : Standard

Work Shift: : 1st Shift

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Relocation Provided: : Yes

Compensation Grade Low: : FR PAY GRADE 27

Compensation Grade High: : FR PAY GRADE 27

Minimum Salary: : $134,900

Maximum Salary: : $210,000

Posting Date : Apr 29, 2024

Minimum Education

Bachelor's degree or equivalent experience

Minimum Experience

6

Summary

Leads innovative statistical and mathematical initiatives to contribute to research and policy through the application of artificial intelligence, machine learning, natural language processing, and conceptual modeling.  Uses existing, and makes improvements to, algorithms to test hypotheses through careful and deliberate model design.  Leads statistical analysis, modeling, and simulation that leads to actionable decisions.  Applies statistical methods to analyze financial stability-relevant topics using large, complex datasets, including unstructured data.  Deploys data mining techniques to refine models that optimize decisions and improve scalable and reusable data mining solutions and capabilities that support Division strategic objectives.  Leads methods for transforming data into actionable information.

Duties and Responsibilities

• Lead the development of analytic projects and predictive modeling using data mining techniques (e.g. classification trees, bagging, random forests, boosting, cluster analysis, factor analysis, shrinkage methods, and generative AI).
• Lead the design and optimization of algorithms for matching and pattern recognition using advanced approaches (e.g. locality-sensitive hashing, fuzzy logic).
• Lead large-scale analytical research projects through all stages; this includes concept formulation, determination of appropriate statistical methodology, data manipulation, research evaluation, and final research report, preferably with familiarity with the Cross Industry Standard Process for Data Mining (CRISP-DM).
• Design, build, and leverage large and complex data sets while thinking strategically about uses of data, and how data usage interacts with data design.
• Lead the transformation of large-scale datasets from internal and external systems in a manner suitable for analysis.
• Lead large-scale data studies and data discovery initiatives targeting for new data sources or new uses of existing data sources such as large language models (LLMs), including unstructured data, and retrieval-augmented generation (RAG). 
• Lead design and implementation of data quality tests and implements new methods to improve statistical inferences of variables across models.
• Visualize and report data findings using a variety of formats to enhance insights into complex issues. Communicates findings through internal reports, executive summaries, and formal presentations.
• Establish links across data sources and map intricate interrelationships.
• Compile, review, and assess information from academic journals, market sources, and other reports to maintain state-of-the-art knowledge in data analysis techniques.
• Present at academic conferences and publish in peer-reviewed academic journals in relevant subject fields in economics, finance, or computer science. 
• Contribute to staff training on new and emerging technologies such as cloud computing, artificial intelligence, and machine learning.

The Financial Stability Research, Data, and Technology (RDaTA) section is seeking a Senior Data Scientist whose primary role will be to lead innovative statistical and mathematical initiatives to contribute to research and policy through the application of artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and conceptual modeling. The Senior Data Scientist will also use existing, and make improvements to, algorithms to test hypotheses through careful and deliberate model design and lead statistical analysis, modeling, and simulation that leads to actionable decisions. They will also apply statistical methods to analyze financial stability-relevant topics using large, complex datasets, including unstructured data and deploy data mining techniques to refine models that optimize decisions and improve scalable and reusable data mining solutions and capabilities that support the Division of Financial Stability’s strategic objectives. 
About the Team
The Financial Stability RDaTA section was founded in 2023. The section is a team of data and technology analysts, and related professionals whose mission is to modernize and maintain the division’s data and technology architectures and to develop data products, data pipelines, tools, and processes that are optimized for financial stability research, analysis, and measurement. The section has an extensive program of analysis, forecasting, and developmental research related to new computing technologies and data. The section collaborates in policy, research, data, and technology primarily with different sections within the Division of Financial Stability and also works extensively with other divisions at the Federal Reserve Board and the Federal Reserve System.
Essential Duties

• Leads the design and optimization of algorithms for matching and pattern recognition using advanced approaches (e.g. locality-sensitive hashing, fuzzy logic) and oversees and contributes to data studies, data discovery initiatives, and data quality investigations.
• Leads large-scale analytical research projects through all stages; this includes concept formulation, determination of appropriate statistical methodology, data manipulation, research evaluation, and final research report, preferably with familiarity with the Cross Industry Standard Process for Data Mining (CRISP-DM).
• Designs, builds, and leverages large and complex data sets while thinking strategically about uses of data, and how data usage interacts with data design.
• Leads the transformation of large-scale datasets from internal and external systems in a manner suitable for analysis.
• Leads large-scale data studies and data discovery initiatives targeting for new data sources or new uses of existing data sources such as large language models (LLMs), including unstructured data, and retrieval-augmented generation (RAG).
• Leads design and implementation of data quality tests and implements new methods to improve statistical inferences of variables across models.
• Visualizes and reports data findings using a variety of formats to enhance insights into complex issues and communicates findings through internal reports, executive summaries, and formal presentations.
• Establishes links across data sources and map intricate interrelationships.
• Compiles, reviews, and assesses information from academic journals, market sources, and other reports to maintain state-of-the-art knowledge in data analysis techniques.
• Presents at academic conferences and publish in peer-reviewed academic journals in relevant subject fields in economics, finance, or computer science.
• Contributes to staff training on new and emerging technologies such as cloud computing, artificial intelligence, and machine learning.

Position Requirements 
FR-27
Bachelor’s degree in computer science or related fields and a minimum of 6 years of related experience, or a master’s degree in a related field and 4 years of related experience, or a PhD degree in a related field and 4 years of related experience, or an equivalent combination of training and related experience. Requires ability to work on extremely complex assignments simultaneously, work on several phases or projects concurrently with the support of others within or outside the section, work with all levels of Board and System staff in support of automation, computing, and database systems, and to work with a high degree of independence to plan and carry out projects with only the overall objectives provided by the supervisor.
Candidates must have strong organizational and oral/written communication skills as well as demonstrated interest in and aptitude for the application of theoretical and quantitative techniques in data science or computer programming. Candidates must also have strong interpersonal, relationship management, and customer service skills with a focus on working effectively in a team environment. Required skills for the position include but are not limited to:

• Demonstrated knowledge of competence in the application of advanced theoretical and quantitative techniques in Data Science, Statistics, Mathematics, Computer Science, or other quantitative discipline achieved by completion of a bachelor’s degree plus six years of experience in the field of banking, finance, supervision, and statistics (or equivalent combination of a higher degree and work experience, such as a master’s degree plus 4 years of experience or a PhD degree plus 4 years of experience).
• Experience with analytical and statistical software packages such as R, MATLAB, or SAS and with programming languages such as Python, Java, or SQL preferred.
• Experience with serverless cloud computing techniques to contribute data to and/or analyze data within a cloud environment.
• Proficient at distributed version control and project management tools (Git/GitLab/Azure DevOps).
• Expertise in LLMs and large datasets and in particular unstructured data such as textual data.
• Expertise in data maintenance and data quality control.
• Excellent analytical and problem-solving skills with attention to detail and data accuracy.
• Work cross-functionally to solve complex problems and improve quality and service.
• Manage multiple projects and work processes in a timely fashion.
• Perform independent research and analysis.
• Requires strong problem solving skills to identify and develop solutions to unique, unfamiliar issues; work output is based on mathematical, statistical, and quantitative methods and preferred knowledge in economics and finance, financial stability, fintech (such as digital assets, crypto currencies, central bank digital currencies (CBDCs), blockchain and Web3 technology), and/or quantum computing.
• Ability to maintain confidentiality and appropriately handle sensitive information.