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

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