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Citi, the leading global bank, has approximately 200 million customer accounts and does business in more than 160 countries and jurisdictions. Citi provides consumers, corporations, governments and institutions with a broad range of financial products and services, including consumer banking and credit, corporate and investment banking, securities brokerage, transaction services, and wealth management. Our core activities are safeguarding assets, lending money, making payments and accessing the capital markets on behalf of our clients.

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Summary:

The Model Analytics Group (MAG) is a growing team. It is responsible for all post model development analytics relating to risk models, including Ongoing Performance Assessments (OPA), Annual Model Reviews (AMR), and Revalidations.

Members of the MAG will be actively involved in different aspects of the model lifecycle. A typical model goes through different stages: design, calibration, testing, documentation, validation, implementation, monitoring and back to re-design and re-calibration when relevant assumptions change or monitoring indicates performance issues. OPAs and AMRs are critical parts of the model lifecycle to determine model performance (model use and monitoring) that can trigger model re-design or re-calibration by identifying critical issues.

Thus the job is related to a critical stage/decision point; essentially whether or not the model can still be used, which in turn has a material impact on resources, timelines, and deliverables.

Key mandates also include driving productivity enhancements in Model Analytics through innovation. The MAG will leverage cutting edge software and technology to reimagine the way we currently execute on this crucial phase of the model lifecycle, to the benefit of our partners, including model sponsors, developers, and Model Risk Management.

Job Description

Key responsibilities:

General:

Partners include various working groups, model developers, risk managers, business clients, model validators, Risk IT, internal and external auditors, and regulators. Engage with partners, as appropriate, to:

• Develop and implement methodologies, algorithms and diagnostic tools for testing model robustness, stability, reliability, performance and quality control of modeling data.
• Conduct on-going model performance analysis,
  • Discover, understand and quantify model limitations,
  • Provide comprehensive interpretations, explanations and conclusions,
  • Work with partners to resolve model issues
• Enhance efficiency and effectiveness of implementation of post model development analytics
  • Automate and consolidate ongoing model analysis and the annual model review process across different models,
  • Migrate analytics to a production environment as appropriate
• Support various tasks in response to regulatory and internal risk management requirements.
• Develop, maintain and enhance technical documentation including project plans, model descriptions, mathematical derivations, data analysis, process and quality controls.

More specifically, develop methodologies, algorithms and diagnostic tools for testing model robustness, stability and performance for the following risk stripes:

• Market Risk:
  • Analysis for market risk models includes, but is not limited to, backtesting and profit attribution analysis (PAA) on hypothetical portfolios for all asset classes across the trading book.
  • Automating and consolidating ongoing model analysis and the annual model review process across different models to enhance efficiency.
  • Collaborating with model developers, market risk managers, model validators, and Risk IT to discover and resolve model issues and enhance existing implementation.
  • Supporting various tasks in response to regulatory and internal risk management requirements.
• Counterparty Credit Risk:
  • Deliver on-going performance test including back-testing and other tests
  • Understand models (pricing/simulation/margin/aggregation model) and the model usages in various applications (CCR capital requirement calculation under Basel III, accounting CVA and internal credit exposure limit monitoring)
  • Understand systems, data flow, data definition and data requirement for various trading products. Utilize this knowledge to perform various analyses to meet risk managers, business and regulators' needs.

Qualifications

• Excellent leadership and managerial skills, including the ability to lead a team to perform testing
• Minimum of a Master's degree in quantitative field (e.g. mathematics, physics, statistics, engineering, economics, finance, financial engineering, etc.) with 2+years of relevant experience.
• Fewer years of relevant experience will be considered for candidates with higher academic qualifications and/or certifications such as a PhD, a second Master's degree, FRM or CFA

• Solid programming skills and experience with statistical and data analysis, modelling techniques and numerical implementations. More specifically experience in Python, R and Perl, shell scripts, UNIX, VBA and basic database skills in either Oracle or Sybase/SQL.
• Experience in developing and maintaining detailed technical documentation for models, model validation, project plans and processes preferred.
• Ability to multi-task, work well under pressure and committed to deliver under tight deadlines
• Strong written and verbal communication skills, and ability to discuss technical issues with partners.
• Strong interpersonal skills and the ability to foster a collaborative environment
• Organized, disciplined and detail oriented with sound problem-solving skills, and the ability to think creatively.
• Keen interest in banking and finance, especially in the field of Risk Management.
• Experience in quantitative finance or a related field, analyzing large and complex data sets, data reliability analysis, quality controls and data processing preferred.
• Experience of one or more of the following is an advantage but not essential: derivative pricing and exotic products; risk management practices and procedures; numerical methods; Monte Carlo simulations; statistical hypotheses testing; trading-book products, risk analytics for wholesale stress testing for credit portfolios, credit risk modelling and risk management or related areas.
• Basic understanding of macroeconomics, monetary economics and econometrics, statistics, time series analysis and Monte Carlo simulations. Familiarity with continuous time models and stochastic processes.

Job Family Group:
Risk Management

Job Family:
Risk Analytics, Modeling, and Validation

Time Type:
Full time

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Qualified applicants will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.

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