Fraud Modeling Analyst

Overview

Developing analytics role within Decision Management (Specialized Analytics) focused on fraud model development, validation and testing, working with Strategy partners and Model Risk Management validators across the model lifecycle.

What you'll do

• Partner closely with Strategy partners and Model Risk Management validators across the model lifecycle, including validation, ongoing performance evaluation, and annual reviews.
• Be responsible for model development, validation and testing of fraud detection models, including statistical analysis, data validation and model performance evaluation.
• Conduct rigorous validation assessment for fraud model validation; develop and implement validation methodologies to assess model accuracy, robustness, and effectiveness.
• Ensure compliance with regulatory requirements and industry best practices in fraud detection and model validation.
• Work with large and complex data sets (internal and external data) to evaluate, recommend, and support the implementation of business strategies.
• Identify and compile data sets using a variety of tools (e.g. Hive, SQL) to help predict, improve, and measure the success of key business-to-business outcomes.
• Document data requirements, data collection, processing and cleaning, and exploratory data analysis, which may include statistical models/algorithms and data visualization techniques.
• Appropriately assess risk when business decisions are made, driving compliance with applicable laws, rules and regulations, adhering to Policy, and escalating, managing and reporting control issues with transparency.

What you'll need

• 3+ years of hands-on analytical experience.
• 2+ years' experience in statistical analysis with working knowledge of at least one of: Python (Must), Hive, Spark, SAS.
• Strong quantitative, analytical, and problem-solving skills; knowledge of probability theory, statistics, mathematical finance, econometrics, numerical methods.
• Strong understanding of statistical concepts and modelling techniques including traditional and advanced ML algorithms.
• Strong proficiency in statistical analysis and model validation techniques.
• Solid grasp of traditional and advanced modeling techniques and algorithms, such as Logistic Regression, Gradient Boosting, Random Forests, etc.
• Expertise in model evaluation techniques such as ROC curves, precision-recall, KS, cross-validation, feature importance, SHAP values, etc.
• Familiarity with regulatory requirements and guidelines related to risk model validation.
• Strong communication and interpersonal skills; experience with stakeholder management.
• Ability to work independently.
• Risk and control mindset: ability to ask incisive questions, assess materiality and escalate issues.
• Ability to handle large volumes of transactional data.
• Demonstrable analytical problem solving and ability to deliver projects in a fast-paced environment.
• Willing to learn and can-do attitude.
• Education: Master's/Bachelor's degree in Statistics, Economics, Finance, Mathematics or a related quantitative field; Engineering/MBA background from a premier institute.

Nice to have

• Familiarity with data extraction tools like Hive.

Details

• Full-time.
• Location: Bangaluru, Karnataka, India.