Applied AI ML Associate
On-site · Bengaluru, Karnataka, India
Job Summary
Own end-to-end onboarding and lifecycle management of vendor models used for fraud and credit risk decisioning. Evaluate and adopt state-of-the-art models and vendor capabilities for fraud and credit risk, providing effective challenge through independent assessment of model methodology, assumptions, inputs, limitations, and performance. Develop in-house fraud and credit risk models end-to-end using statistical and machine learning techniques (e.g., regression, XGBoost/LightGBM, neural networks) across feature engineering, model training, testing/benchmarking, and deployment readiness. Design and execute ongoing performance monitoring for vendor and in-house models across dimensions (time, geography, portfolio/segment, channel, customer, transaction/loan attributes) to track stability, drift, and outcomes. Conduct root-cause analysis for trends and performance shifts, quantify business impact, and communicate findings to senior management. Prepare governance, audit, and regulatory materials supporting model oversight and surveillance. Collaborate with Strategy, Technology, Product Management, Legal, Compliance, Business Management, and Model Governance to meet governance standards and regulatory requirements. Work on multiple projects with modeling teams in other locations to maintain high development standards, reviews, model monitoring, and enhanced usage support in line with Estimation policies and procedures.
Required Qualifications
- 4+ years’ statistical Machine learning model development/validation experience in a deeply quantitative role in the financial services industry or Fintech’s dealing with advanced analytical or machine learning methods
- A Master’s or Ph.D. Degree in a technical or quantitative field such as Statistics, Economics, Finance, Mathematics, Computer Science, Engineering from Top-Tier university
- Proficient in Python, with hands-on experience in data analysis and writing production-quality code
- Extensive experience with machine learning and data analysis toolkits (e.g., NumPy, Scikit-Learn, Pandas)
- Ability to effectively leverage Generative AI tools to enhance productivity, analysis, and problem-solving in day-to-day work
- Strong written and spoken communication skills to effectively convey technical concepts and results to both technical and business audiences
Desired Qualifications
- 4+ years’ statistical Machine learning model development/validation experience in a deeply quantitative role in the financial services industry or Fintech’s dealing with advanced analytical or machine learning methods
- A Master’s or Ph.D. Degree in a technical or quantitative field such as Statistics, Economics, Finance, Mathematics, Computer Science, Engineering from Top-Tier university
- Proficient in Python, with hands-on experience in data analysis and writing production-quality code
- Extensive experience with machine learning and data analysis toolkits (e.g., NumPy, Scikit-Learn, Pandas)
- Ability to effectively leverage Generative AI tools to enhance productivity, analysis, and problem-solving in day-to-day work
- Strong written and spoken communication skills to effectively convey technical concepts and results to both technical and business audiences
Apply with one swipe on Sorce. We auto-fill applications and apply on your behalf — no cover letters, no 40-minute forms.
Hiring someone like this?
Get your role in front of qualified candidates on Sorce.