Stress Testing 2nd LOD Senior Analyst

Citi•Irving, TX

13h•Hybrid

About The Position

Citibank, N.A. seeks a Stress Testing 2nd LOD Senior Analyst to conduct statistical analysis for risk-related projects and build quantitative forecasting models to support the bank’s risk management objectives, specifically for Market Risk, Credit Risk, and Operational Risk. The role involves generating stress testing scenarios for regulatory frameworks such as CCAR, CECL, IFRS9, and GSST. The analyst will present and formally document the results of forecasting scenarios for model validation and performance monitoring, analyze and interpret data reports to identify trends, and present findings to senior management. Key responsibilities also include validating model assumptions, escalating identified risks, automating data extraction and pre-processing using ETL tools and programming languages (R, Python, Visual Basic), and performing ad hoc statistical analyses. The position requires designing, maintaining, and optimizing complex data manipulation processes with SQL and Python, visualizing analytical results using various tools (Excel, Tableau, Python, R), and conducting statistical research to enhance methodologies. Furthermore, the analyst will develop model performance back-testing packages, utilize predictive modeling methods (time-series, logistic regression), implement model monitoring metrics, prepare and validate large-scale datasets, document analytical methodologies, and collaborate with model developers, validators, and technology teams to unify stress testing methodologies.

Requirements

Master’s degree, or foreign equivalent, in Economics, Finance, Mathematics, or related field and 2 years of experience as a Model/Analysis/Validation Senior Analyst, Model Developer, Country Finance Officer, Financial Analyst, Economics Analyst, or related position involving quantitative data manipulation and analysis, model development, and economic research for financial forecasting generation.
Alternatively, a Bachelor’s degree in Economics, Finance, Mathematics, or related field and 5 years of progressively responsible, post-baccalaureate experience in the listed positions.
Full span of experience must include: Predictive modelling methods: Time-series, regression, and logistic regression.
Full span of experience must include: Automating data extraction and data pre-processing.
Full span of experience must include: SQL, R.
Full span of experience must include: Data validation and data quality issues identification and control.
1 year of experience must include: Developing and validating methods of measuring and analyzing market risk, credit risk and operation risk.
1 year of experience must include: Banking regulations including CCAR, CECL, IFRS9 and GSST.
1 year of experience must include: Statistical analysis, data modeling and validation.
1 year of experience must include: Model risk monitoring, limitations assessment, and overlays management.
1 year of experience must include: Conducting economic research, including macroeconomics markets.
1 year of experience must include: SAS, Python.

Responsibilities

Conduct statistical analysis for risk-related projects.
Build quantitative forecasting models to support the bank’s risk management objectives, used to assess Market Risk, Credit Risk, and Operational Risk.
Generate stress testing scenario for regulatory frameworks including Comprehensive Capital Analysis and Review (CCAR), Current Expected Credit Losses (CECL), Internal Financial Standing Reporting 9 (IFRS9), and Global Systemic Stress Testing (GSST).
Present the results of various forecasting scenarios produced by statistical models.
Write formal documentation the scenarios for model validation and performance monitoring as part of the risk management policy and procedures.
Analyze and interpret the data reports generated from various internal and external data sources to identify trends and present quantitative findings to senior management.
Validate model assumptions and escalate any identified risks and sensitive areas in the statistical modeling methodology and process.
Automate data extraction and data pre-processing tasks using ETL tools including R, and Python and Visual Basic programming languages to achieve efficiency in business process.
Perform ad hoc statistical analyses, scenario tests, and sensitivity assessments.
Design, maintain and optimize complex data manipulation processes including structured ETL pipelines and automated workflows using SQL and Python to support model development.
Visualize analytical results using Excel, Tableau, Python, R and present the quantitative findings to senior management.
Conduct statistical research to evaluate model performance, enhance methodologies, and refine forecasting frameworks for Market, Credit and Operational Risk.
Develop model performance back-testing packages and program validation-ready analytical materials.
Use of predictive modeling methods, including time-series analysis and logistic regression, to measure credit and market risk.
Implement model monitoring metrics, including stability, performance, and threshold tests, to ensure algorithmic accuracy.
Prepare and validate large-scale datasets, performing data quality checks and implementing data controls to ensure analytical accuracy.
Document analytical methodologies to ensure reproducibility, transparency, and technical compliance with internal model risk management policies.
Collaborate with model developers, validators, and technology teams to address data implementation issues and unify stress testing methodologies across products.

Benefits

discretionary and formulaic incentive and retention awards
medical coverage
dental coverage
vision coverage
401(k)
life insurance
accident insurance
disability insurance
wellness programs
paid time off packages, including planned time off (vacation), unplanned time off (sick leave)
paid holidays