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Quant in Model Analysis Group

Job Req ID 21307400 Primary Location Warsaw, Poland Job Category Risk Management
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The Model Analysis Group (MAG) is a growing team within Risk Modeling & Analytics.

It is responsible for all post model development analytics relating to models developed by Risk Modeling & Analytics teams, 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 Analysis 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.

As the MAG spans all Risk Modeling & Analytics models, members of the MAG will have the opportunity to become well-versed in multiple risk stripes, with such skills training enhancing their mobility and growth potential within Risk Modeling & Analytics and the larger Risk organization.

The position will work very closely with others in the MAG, including the Head of MAG who reports to the head of Risk Modeling & Analytics.  Risk Modeling & Analytics develops risk analytics for use by Risk, Finance and Product and Client Coverage teams on a global basis. The head of Risk Modeling & Analytics reports to the Finance Chief Risk Officer.

Key responsibilities:


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 modelling 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:

  • Retail: Retail models include Basel, Risk Capital, Internal Stress Testing (GSST) and related component models.  These models are applied to all delinquency managed portfolios across the globe and cover upwards of $320 billion in exposure. 

  • Foundational Risk & Reserves: FR&R models include loss likelihood and severity methodologies and applications, including methodologies used for CECL and IFRS 9, as well as foundational measures of risk (PD, LGD, CCF).

  • Credit and Risk Rating Analytics: CRRA models include one-year probability of default models and Expert Judgment Risk Rating Methodologies used for low data / low default portfolios.

  • Wholesale Credit Stress Testing (WCST) Includes CCAR and ICAAP models


  • Educated to a postgraduate level, with an excellent academic record in a mathematical and quantitative field (e.g. mathematics, physics, statistics, engineering, economics, finance, financial engineering, etc.). Masters or Ph.D. is strongly preferred.

  • 2+ years of relevant experience

  • Solid programming skills and experience with statistical and data analysis, modelling techniques and numerical implementations.  More specifically experience in  Python/R.  Knowledge of C/C++, Java, SAS, Perl, shell scripts, UNIX, VBA and basic database skills in either Oracle or Sybase/SQL would be a plus.

  • Experience in developing and maintaining detailed technical documentation for models, model validation, project plans and processes preferred.

  • Ability to meet deadlines for product deliverables in a timely, proactive and entrepreneurial manor.

  • 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; banking-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.  

  • Fewer years’ experience considered with advanced degrees.

What we offer:

  • Work in a challenging area of the financial industry with one of the world's leading companies with exposure to variety of products, processes and controls

  • Cooperation with a high quality, international, multicultural and global team

  • Work in a friendly and diversified environment, appreciating differences in style and perspective and using them to add value to decisions leading to organizational success

  • Management supporting balanced and agile work (flexible working hours, home office)

  • Attractive benefits package (Benefit System, medical care, pension plan etc.)

  • A chance to make a difference with various affinity networks and charity initiatives


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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