· Conduct independent oversight of enterprise-wide models with a focus on Artificial Intelligence and Machine Learning based models for credit, fraud, or other business/ risk types.

· Conduct gap assessments and establish robust framework to strengthen model risk controls and meet heightened regulatory standards

· Conduct research to explore opportunities to elevate model excellence and drive business impact

· Seek and incorporate external perspective in day-to-day work and projects

· Communicating results to partners, senior leadership and various model committees

Critical Factors to Success:

Business Outcomes: Effectively challenge the conceptual soundness, theory and approach, purpose/usages of predictive models

Maximize business returns by institutionalizing efficient and accurate models.

Innovate modeling techniques and variable creation

• Ensure modeling accuracy and enhance modeling efficiency in existing processes using Machine Learning

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• Leadership Outcomes: Put enterprise thinking first, connect the role’s agenda to enterprise priorities and balance the needs of customers, partners, colleagues & shareholders.

• Lead with an external perspective, challenge status quo and bring continuous innovation to our existing offerings

• Demonstrate learning agility, make decisions quickly and with the highest level of integrity

• Lead with a digital mindset and deliver the world’s best customer experiences every day

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Past Experience: 0-2 years’ experience in credit and fraud analytics, machine learning or both - Academic Background: B.tech/B.Eng, MBA, Master’s Degree In Economics, Statistics Or Related Fields From Top Tier Institute

Functional Skills/Capabilities:

• Hands-on model development or validation experience.

• Strong Analytical and Relationship and project management skills for driving validation initiatives.

• Experience in applying advanced statistical and/or quantitative techniques to solve business problems is preferred.

• Good Verbal, Written, Interpersonal skills and ability to work effectively in a team environment.

• Willingness to Collaborate with Cross-Functional teams to drive validation and Project Execution.

• Effectively communicating complex Analytical results to Business Partners and Senior Management.

• Flexibility and Adaptability to Work Within tight deadlines and changing priorities. –

Technical Skills/Capabilities:-

• Experience with at-least one of the data manipulation tools such as R, Python, SQL and SAS is a must have.

• Data Science/ Machine Learning/ Artificial Intelligence, Expertise in Coding, Supervised and Unsupervised Techniques - active learning, transfer learning, neural models, decision trees, reinforcement learning, graphical models, Gaussian processes, Bayesian models, map reduce techniques, Random Forest, Gradient Boosting, Deep Learning, Text Mining Algorithms.