Fraud Modeling Analyst
Citi (company site, opens in a new tab)3+ yrsKarnatakaOn-siteDirect career pageEst. 12–22 LPA
Posted 15h ago
Reach people at Citi
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Citi you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Develop, validate, and test fraud detection models, including statistical analysis and performance evaluation.
- Conduct rigorous validation assessments ensuring compliance with regulatory requirements and industry best practices.
- Partner with Strategy and Model Risk Management teams to manage model lifecycle, including validation, performance evaluation, and annual reviews.
Minimum Requirements
- Minimum 3+ years hands-on analytical experience including 2+ years of statistical analysis using Python (mandatory), Hive, Spark, or SAS.
- Bachelor’s or Master’s degree in Statistics, Economics, Finance, Mathematics or related quantitative field; Engineering or MBA from premier institutes also acceptable.
- Strong knowledge of statistical concepts, modeling techniques (e.g. Logistic Regression, GBM, Random Forests) and model validation methods (ROC, KS, cross-validation, SHAP).
- Familiarity with regulatory guidelines for risk model validation and experience handling large transactional datasets.
Ideal Candidate Profile
- Experienced in fraud model development and validation in regulated financial environments.
- Proficient in statistical programming with strong problem-solving skills and risk/control mindset.
- Able to work independently managing multiple stakeholders and delivering in fast-paced, complex data environments.