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EXL is seeking a Senior Analytics Consultant in India to lead analytics engagements for banking clients, focusing on credit risk, fraud detection, or marketing analytics. You will design and implement advanced models and translate insights into strategic recommendations.
This client-facing role requires strong SQL and proficiency in Python, R, or SAS, plus hands-on experience with large datasets, model development, and regulatory considerations.
Job Title: Senior Analytics Consultant – Banking (Risk/Fraud/Marketing Analytics)
Location: Bangalore/Gurgaon (WFO)
Job Type: Full-Time
Role Overview: We are looking for a Senior Analytics Consultant with over 5 years of experience in banking analytics, specifically in areas such as credit risk modeling, fraud detection, or marketing analytics. This is a client-facing role where you’ll lead analytics engagements, design and implement advanced models, and translate data insights into strategic recommendations for banking clients.
Lead and manage analytics projects in the banking sector with a focus on credit risk (e.g., scorecards, PD/LGD/EAD models), fraud analytics (e.g., real-time detection, anomaly detection), or marketing analytics (e.g., customer segmentation, CLTV, campaign targeting). Collaborate with clients to define business problems, develop analytic frameworks, and deliver actionable solutions. Build and validate statistical and machine learning models using Python, R, or SAS. Translate complex analytical findings into clear, business-friendly presentations and reports. Guide junior team members and contribute to knowledge sharing within the firm. Ensure compliance with regulatory standards (e.g., Basel II/III, CCAR, AML) in risk and fraud modeling.
Bachelor’s or Master’s degree in Statistics, Economics, Mathematics, Computer Science, or a related quantitative discipline. 5+ years of hands‑on experience in banking analytics with a focus on one or more of the following: credit risk, fraud detection, or marketing. Proficiency in SQL and at least one analytical programming language (Python, R, or SAS). Strong understanding of statistical modeling techniques and machine learning algorithms. Experience working with large datasets and conducting data wrangling, feature engineering, and validation. Excellent communication and presentation skills, especially in client-facing settings. Strong understanding of the banking regulatory environment.
Prior experience in a consulting or professional services environment. Familiarity with cloud-based analytics platforms (e.g., AWS, Azure) and big data tools (e.g., Hive, Spark). Experience with regulatory model validation or governance frameworks. Exposure to digital banking data sources and real-time analytics.