Vice President - Fraud Business Analytics

Discover your future at Citi

Working at Citi is far more than just a job. A career with us means joining a team of more than 230,000 dedicated people from around the globe. At Citi, you’ll have the opportunity to grow your career, give back to your community and make a real impact.

Job Overview

Whether you’re at the start of your career or looking to discover your next adventure, your story begins here.  At Citi, you’ll have the opportunity to expand your skills and make a difference at one of the world’s most global banks.  We’re fully committed to supporting your growth and development from the start with extensive on-the-job training and exposure to senior leaders, as well as more traditional learning.  You’ll also have the chance to give back and make a positive impact where we live and work through volunteerism.

Responsibilities:

• Predictive Model Development:Design, develop, validate, and deploy statistical models, with a strong focus onLogistic RegressionandDecision Treealgorithms, to identify and predict fraudulent activities across various financial products and channels.

• Behavioral Segmentation:Apply advancedsegmentationtechniques (e.g., clustering, rule-based segmentation) to stratify customer populations and transaction behaviors, enabling more precise fraud risk assessment and targeted prevention strategies.

• Rule Engine Optimization:Translate model outputs and analytical insights into actionable fraud rules and strategies within fraud detection systems, continuously monitoring their performance and tuning for optimal balance between fraud capture and false positive rates.

• Performance Monitoring & Tuning:Establish robust monitoring frameworks for all deployed models and strategies, regularly assessing their effectiveness, recalibrating models, and performing A/B testing to drive continuous improvement.

• Emerging Trend Analysis:Proactively analyze large and complex datasets to identify new fraud patterns, emerging threats, and vulnerabilities, providing rapid analytical responses to evolving fraud typologies.

• Data Exploration & Feature Engineering:Conduct extensive data exploration and feature engineering to identify powerful variables and attributes that enhance the predictive power of fraud models.

• Cross-Functional Collaboration:Partner closely with Fraud Operations, Risk Management, Product, Technology, and Data Engineering teams to understand business requirements, integrate analytical solutions, and ensure data quality and availability.

• Reporting & Communication:Prepare and present clear, concise, and compelling reports, dashboards, and presentations on model performance, fraud trends, and strategic recommendations to both technical and non-technical stakeholders, including senior leadership.

Qualifications:

• 15+ years of experience in a fraud analytics, business/risk analytics, or similar quantitative role, with a proven track record of applying statistical modeling techniques in financial services

• Demonstrated hands-on experience developing and deploying predictive models using Logistic Regression and Decision Tree algorithms.

• Strong expertise in various segmentation methodologies and their application in risk or fraud analytics.

• Proficiency in statistical programming languages such as Python (with libraries like scikit-learn, Pandas, NumPy, SciPy) or R for data manipulation, statistical analysis, and model building.

• Experience with other machine learning algorithms relevant to fraud detection (e.g., Random Forests, Gradient Boosting, SVMs, clustering).

• Familiarity with big data technologies (e.g., Spark, Hadoop) and cloud platforms (AWS, Azure, GCP).

• Prior experience in the financial services, e-commerce, or payments industry with specific knowledge of fraud typologies (e.g., card fraud, account takeover, application fraud, synthetic identity).

• Experience working with fraud detection platforms and rule engines.

• Advanced SQL skills for complex data extraction and analysis from large relational and non-relational databases.

• Experience with data visualization tools (e.g., Tableau, Power BI, Qlik Sense) to create actionable dashboards and reports.

• Solid understanding of model validation techniques, performance metrics (e.g., AUC, Gini, Precision-Recall), and their interpretation.

• Strong analytical, problem-solving, and critical thinking skills, with an ability to work with ambiguous data and derive meaningful insights.

• Excellent communication and presentation skills, capable of explaining complex analytical concepts to diverse audiences.

• Experience analyzing large datasets; applying mathematical, statistical and quantitative analysis techniques to perform complex analyses and data mining

Education:

• Master’s/Bachelor’s/PhD degree in Statistics, Economics, Finance, Mathematics or a related quantitative field; Engineering/MBA background from a premier institute 

Working at Citi is far more than just a job. A career with us means joining a family of more than 230,000 dedicated people from around the globe. At Citi, you’ll have the opportunity to grow your career, give back to your community and make a real impact.

Take the next step in your career, apply for this role at Citi today

https://jobs.citi.com/dei

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Job Family Group:

Decision Management

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Job Family:

Business Analysis

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Time Type:

Full time

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Most Relevant Skills

Please see the requirements listed above.

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Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.

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