Job Location: Kolkata

With a startup spirit and 90,000+ curious and courageous minds, we have the expertise to go deep with the worlds biggest brandsand we have fun doing it. Now, were calling all you rule-breakers and risk-takers who see the world differently and are bold enough to reinvent it. Come, transform with us.

Are you the one we are looking for?

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In this role, you will be responsible for AML Model Management for a BFS client.


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You will be primarily working for the centralized advanced analytics team of a banking firm to provide machine learning deployment, testing support to leading edge Enterprise AML team. The role will require interacting with various business units including their risk, finance, controllership stakeholders etc. You will also be responsible for coordinating with auditors and model development teams to ensure the AML and Enterprise Modeling Governance standards are followed; Your activities will include, but will not be limited to the following:

  • Works closely with different verticals such as, Enterprises AML team, subject matter expertise/ research team, model development team to have broad model understanding, downstream use, monitoring procedure, and support in machine learning model deployment, UAT testing, error handling and maintain performance standards
  • Working knowledge, end-to-end understanding of AML team functioning, compliance and regulatory standards
  • Support in in-house machine learning platform maintenance and quality standards, adhere auditor and regulatory requirements, identify potential control gaps
  • Collaborate with AML team to build data and model pipelines, manage the infrastructure and data pipelines needed to bring code to production
  • Research and implement best practices to improve the existing machine learning infrastructure, bring in agile practice, overall process efficiency

Qualifications we seek in you

Minimum qualifications

  • 2+ Years of Experience in BFS Risk analytics preferably in AML area
  • Minimum of a Master of Science in Statistics/Economics/Finance or Bachelors in Engineering, Computer Sciences, Information Management
  • Hands on experience in JavaScript, SQL, Python, SAS working with large data sets
  • Expertise on computer science fundamentals, covering data structures, algorithms, computability and complexity and computer architecture, experience in exceptional mathematical skills, in order to perform computations and work with the algorithms involved in this type of programming
  • Provided support to model development team in implementing machine learning in production, collaborated in building data and model pipelines, managed the infrastructure and data pipelines required to bring code to production, maintained scalable machine learning solutions in production
  • Deployed machine learning algorithms and libraries along with testing, error handling, maintaining performance standards
  • Good client management and communication/presentation skills – written & verbal; proficient project management skills, timely and proactive communication
  • Self-driven, proactive, “can-do” attitude. Ability to work under ambiguity and with minimal supervision

Preferred qualifications

  • Machine Learning algorithm, modelling experience would be preferred
  • Knowledge in overall risk management of anti-money laundering related models, AML regulatory, compliance and governance would be preferred
  • Hands-on Experience in dashboard usage and design experience, such as Spotfire, Tableau

Genpact is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color, religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws. Genpact is committed to creating a dynamic work environment that values diversity and inclusion, respect and integrity, customer focus, and innovation. For more information, visit www.genpact.com. Follow us on Twitter, Facebook, LinkedIn, and YouTube.

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