What you would do in this role
Engagement Execution
- Lead client engagements that may involve model development, validation, governance, strategy, transformation, implementation and end-to-end delivery of fraud analytics/management solutions
- Advise clients on a wide range of Fraud Management/ Analytics initiatives. Projects may involve Fraud Management advisory work for CXOs, etc. to achieve a variety of business and operational outcomes.
- Develop and frame Proof of Concept for key clients, where applicable
Practice Enablement
- Mentor, groom and counsel analysts and consultants.
- Support development of the Practice by driving innovations, initiatives.
- Develop thought capital and disseminate information around current and emerging trends in Fraud Analytics and Management
- Support efforts of sales team to identify and win potential opportunities by assisting with RFPs, RFI. Assist in designing POVs, GTM collateral.
Who we are looking for
- Fraud Analytics experience at one or more Financial Services firms, or Professional Services / Risk Advisory with significant exposure to one or more of the following areas:
- Banking Fraud, Payment Fraud, Credit Card Fraud, Retail Fraud, Anti Money Laundering, Financial Crime, Telecom Fraud, Energy Fraud, Insurance Claims Fraud etc.
- Advanced skills in development and validation of fraud analytics models, strategies, visualizations.
- Understanding of new/ evolving methodologies/tools/technologies in the Fraud management space.
- Expertise in one or more domain/industry including regulations, frameworks etc.
- Experience in building models using AI/ML methodologies
Modeling:
- Experience in one or more of analytical tools such as SAS, R, Python, SQL, etc.
- Knowledge of data processes, ETL and tools/ vendor products such as VISA AA, FICO Falcon, EWS, RSA, IBM Trusteer, SAS AML, Quantexa, Ripjar, Actimize etc.
- Proven experience in one of data engineering, data governance, data science roles
- Experience in Generative AI or Central / Supervisory banking is a plus.
- Strong conceptual knowledge and practical experience in the Development, Validation and Deployment of ML/AL models
- Hands-on programming experience with any of the analytics tools and visualization tools (Python, R, PySpark, SAS, SQL, PowerBI/ Tableau)
- Knowledge of big data, ML ops and cloud platforms (Azure/GCP/AWS)
- Strong written and oral communication skills
- Project management skills and the ability to manage multiple tasks concurrently
- Strong delivery experience of short and long term analytics projects
