Sr. Data Scientist, Stress Testing Production and Analysis- Division of Supervision and Regulation - 23008

DESCRIPTION/RESPONSIBILITIES:
Leads statistical and mathematical initiatives to predict future outcomes through the application of 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 lead to actionable decisions. Applies statistical methods to characterize uncertainty using large, complex datasets. 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.

Principal Duties and Responsibilities
1. 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).
2. Lead the design and optimization of algorithms for matching and pattern recognition using advanced approaches (e.g. locality-sensitive hashing, fuzzy logic).
3. 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.
4. Design, build, and leverage large and complex data sets while thinking strategically about uses of data, and how data usage interacts with data design.
5. Lead the transformation of large-scale datasets from internal and external systems in a manner suitable for analysis.
6. Lead large-scale data studies and data discovery initiatives targeting for new data sources or new uses of existing data sources.
7. Lead design and implementation of data quality tests and implements new methods to improve statistical inferences of variables across models.
8. 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.
9. Establish links across data sources and map intricate interrelationships.
10. Compile, review, and assess information from academic journals, market sources, and other reports to maintain state-of-the-art knowledge in data analysis techniques.
This description is intended to indicate the general level and function of this job. It is not intended to be all­ inclusive, and employees may be assigned duties not listed.

REQUIRED SKILLS:
Position Qualifications: Must demonstrate knowledge of competence in the application of advanced theoretical and quantitative techniques in Data Science, Statistics, Mathematics, Computer Science, or other quantitative discipline typically achieved by completion of a master's degree plus four years of experience the field of banking, finance, supervision,  or statistics (or equivalent work experience).  Experience with analytical and statistical software packages such as R, MATLAB, or SAS.  Experience with programming languages such as Python, Java, or SQL preferred.  Extensive experience with large datasets.  Passionate about data maintenance and data quality control.  Excellent analytical and problem solving skills with attention to detail and data accuracy.  Strong interpersonal, communication (verbal and written), relationship management, and customer service skills with a focus on working effectively in a team environment. Work cross-functionally to solve complex problems and improve quality and service.  Manage multiple projects and work processes in a timely fashion.  Perform involved and independent research and analysis.  Ability to maintain confidentiality and appropriately handle sensitive information. (FR-27) or Lead involved and independent research and analysis.  Maintain confidentiality and appropriately handle sensitive information.(FR-28)

Remarks
This Data Scientist role in the Stress Testing section in Supervision and Regulation will support the supervisory stress tests of bank portfolios related to the Federal Reserve’s responsibilities under the Dodd-Frank Act and ongoing bank supervision.

The analyst will be assigned to perform the following duties:
• implement, modify, test, and document production models and systems used in the stress test
• execute stress test models and conduct analysis of model outputs to better inform ongoing bank supervision
• assess and analyze regulatory data and other data used in the stress test
• oversee and mentor analysts engaged in these activities

The ideal candidate will have:
• a high level of intellectual curiosity
• a demonstrated ability and desire to lead and mentor
• strong analytical and communication skills
• ability to write, communicate clearly, and deliver effective presentations
• strong interpersonal skills, including the ability to collaborate well across teams and organizations in a matrix environment, while accomplishing multiple goals within established and changing deadlines
• a demonstrated ability to conduct analysis of financial data using large datasets
• a demonstrated experience in statistical modeling, with knowledge of statistical and econometric modeling techniques and approaches.
• a love of coding
• expertise in one or more statistical programming languages (R preferred) is required, and the ideal candidate will have experience using scripting languages (Python preferred), Linux, and a version control system (Git preferred)
• experience with database management tools (such as Microsoft SQL Server)

The position will require flexibility to work extended hours to meet deadlines, especially during the stress test production quarter (currently April - June).  

Additionally, the ideal candidate will meet a number of the following:
• experience with modern revenue and/or risk modeling practices and industry standards
• experience with bank regulatory capital measures and US GAAP accounting standards
• experience with software development best practices such as software development life cycle (SDLC)
• experience with system design in a cloud-based computing environment.

An assessment or data analysis exercise may be part of the application process.

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