Wholesale Credit Loss Modeling – VP – Hybrid

DART (Risk Data, Analytics, Reporting & Technology) is the leading risk modelling and data analytics team in Citi. We use mathematical modelling and the latest technologies to calculate risk for the largest portfolios in Citi. Our models and analytics ensure that the bank has adequate capital during crisis.

We are a diverse group of professionals with backgrounds in physics, engineering, finance, economics, and data science. You will work alongside experienced colleagues to further develop your analytical and quantitative skills. Your responsibilities will include building models and analytical applications to tackle real-world challenges, paving the way for a career as a risk management expert and leader.

The successful candidate will fill a VP-level role as part of the Wholesale Credit Loss Forecasting Analytics (LFA) Team within the Credit Climate and Obligor Risk Analytics (CORA) Organization in DART.  The LFA team is responsible for models used in reserves calculation (CECL & IFRS 9) and stress testing (CCAR, ICAAP, and others). The role offers a huge development opportunity and work as part of local and global initiatives.

Responsibilities:

• Research, develop, and test wholesale expected credit loss models in line with requirements for CECL credit loss reserves, CCAR regulatory stress testing, and other purposes including internal stress testing.
• Implement credit loss models in Python or other languages for model execution, testing, and analytical support.
• Prepare detailed quantitative modeling and analysis for risk managers and senior management.
• Present complex risk models and results in written documentation and live presentations.
• Conduct statistical analysis, quantitative modeling, and model risk controls.
• Work with risk managers, businesses, and technology to design and build models for risk capture and stress testing.

Qualifications:

• Master’s or Doctoral degree in a quantitative field (Mathematics, Physics, Computer Science, Econometrics, Statistics, Economics, Finance, etc.) is preferred. Bachelors Degree is required.
• 3+ years of experience in quantitative financial modeling. Hands-on experience with the research, development, and implementation of financial models.
• Ability to apply sophisticated mathematical/analytical techniques to solve real-world problems.
• Knowledge of wholesale credit products and financial markets at a financial institution is preferred.
• Good knowledge of credit reserves calculation in line with CECL/IFRS 9, bank stress testing in line with CCAR/ICAAP, or PD/LGD/EAD modeling is a plus.
• Familiar with statistics packages and regression models.
• Strong programming skills in Python. Good knowledge of Linux is a plus.
• Excellent communication skills, verbal as well as written.

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.

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In addition to salary, Citi’s offerings may also include, for eligible employees, discretionary and formulaic incentive and retention awards. Citi offers competitive employee benefits, including: medical, dental & vision coverage; 401(k); life, accident, and disability insurance; and wellness programs. Citi also offers paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays. For additional information regarding Citi employee benefits, please visit citibenefits.com. Available offerings may vary by jurisdiction, job level, and date of hire.

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Anticipated Posting Close Date:

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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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