About the role

We’re currently looking for a high caliber professional to join our team as Vice President - Fraud Business Analytics - Hybrid. (Internal Job Title: Vice President - C13) based in Bangalore - India. Being part of our team means that we’ll provide you with the resources to meet your unique needs, empower you to make healthy decision and manage your financial well-being to help plan for your future. For instance:

Citi provides programs and services for your physical and mental well-being including access to telehealth options, health advocates, confidential counseling and more. Coverage varies by country.
We empower our employees to manage their financial well-being and help them plan for the future.
Citi provides access to an array of learning and development resources to help broaden and deepen your skills and knowledge as your career progresses.
We have a variety of programs that help employees balance their work and life, including generous paid time off packages.

In this role, you’re expected to:

Citi Analytics & Information Management (AIM) team is a global community that objectively connects and analyzes information to create actionable intelligence for our business leaders. It identifies fact-based opportunities for revenue growth and expense reduction in partnership with the businesses.

We are seeking a highly skilled and analytically driven Business Analytics specializing in the Fraud domain. This role requires expertise in statistical modeling, particularly Decision Trees, Logistic Regression, and Segmentation techniques. In this pivotal role, you will leverage advanced analytical methods to develop, implement, and optimize fraud detection and prevention strategies. You will be responsible for building predictive models, segmenting customer behaviors and transactions to identify high-risk areas, and providing actionable insights to significantly reduce fraud losses while minimizing impact on legitimate customers. This position requires a strong blend of business analytics principles, combined with a keen interest or experience in fraud detection and prevention, an understanding of fraud typologies, and the ability to translate complex analytical findings into practical business solutions.

This is an individual contributor role.

Responsibilities:

Predictive Model Development: Design, develop, validate, and deploy statistical models, with a strong focus on Logistic Regression and Decision Tree algorithms, to identify and predict fraudulent activities across various financial products and channels.
Behavioral Segmentation: Apply advanced segmentation techniques (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.