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VP, Model Analysis Validation Officer

Irving, TX, USA | Citi Private Bank

  • Industry:
    Financial Services
  • Position Type:
    Full-Time
  • Functions:
    Risk Management
  • Experience:
    3-5 years
Job Description:
68 people have viewed this job

Provide effective challenge on the conceptual soundness of models developed using classical techniques such as logistic / linear regression, decision tree, clustering, as well as advanced machine learning techniques such as gradient boosting, random forest.

Perform end-to-end model validation activities for new and existing models, including: new model validation, annual model review, ongoing monitoring, and mode change reviews.

Utilize analytical packages such as SAS, R, Python to conduct validation analyses, and use Citi’s proprietary model governance system to perform validation activities.

Enhance tools and processes to promote compliance with internal and regulatory model risk management policy and guidance.

There will be plenty of learning and growth opportunities with this position, from both technical and leadership perspective. The incumbent will be exposed to different areas of business operations and a variety of modeling approaches, including machine learning on top of industry standard tools.

Qualification:

§ Minimum of a Master degree, preferably a PhD, in a quantitative field (e.g. Statistics, Economics, Mathematics, Physics, Engineering, etc.)

§ At least 3 years of experience in predictive modeling, preferably some experience in Model Risk Management with model validation responsibilities. 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, CPA or CFA 

§ Sound knowledge of Statistics& Econometrics theories and techniques, experience with Data Mining & Machine Learning methods a plus

§ Strong written and oral communication, and project management skills

§ Ability to collaborate with stakeholders with various background, and to effectively explain technical terms to audience with different levels of technical knowledge

§ Proficiency in Statistical / Modeling programs such as SAS, R, Python


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