Executive Director - Credit Risk Analytics at Morgan Stanley

Mumbai, maharashtra, India -

Full Time

Start Date

Immediate

Expiry Date

03 Mar, 26

Salary

0.0

Posted On

03 Dec, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Credit Risk Management, Risk Analytics, Team Leadership, Model Governance, Regulatory Engagement, Statistical Analysis, Programming, Machine Learning, Quantitative Techniques, Portfolio Credit Risk, Data Analysis, Credit Scoring, Counterparty Credit Risk, Strategic Thinking, Change Management, Collaboration

Industry

Financial Services

Description

Team Leadership: Manage and mentor a diverse team of credit risk analytics professionals, fostering a culture of excellence and innovation. Provide direction and oversight across specialized functions including IRB modeling, counterparty credit risk analytics and credit stress testing/provisioning. Develop team members' skills and careers, and promote an inclusive, collaborative environment. Strategic Direction: Drive the strategic development of the Firms credit risk measurement methodologies. Set the vision for next-generation credit risk models and analytics tools, ensuring they align with business objectives and emerging risks. Continuously improve model frameworks to enhance risk-adjusted return on capital and support prudent risk taking by the Firm. Regulatory Engagement: Lead the Credit Risk Analytics teams response to regulatory initiatives and requirements. Oversee the implementation of frameworks such as Basel III Internal Ratings-Based approaches and stress testing programs. Engage with regulators and internal Model Risk Management on model approvals, validation exercises, and regulatory examinations. Ensure all credit risk models and processes meet the highest standards of compliance and transparency. Cross-Functional Partnership: Work closely with a broad range of stakeholders to integrate credit risk insights into decision-making. Partner with Credit Risk Managers and business units to ensure models adequately capture risks and inform credit decisions and limit setting. Collaborate with Technology teams to drive the development of analytics infrastructure and data capabilities. Provide senior leadership with timely, insightful analysis of credit risk findings and trends. Model Governance: Oversee the end-to-end lifecycle of credit risk models, from design and development through validation, implementation, and ongoing performance monitoring. Chair internal model committees and ensure methodological rigor, robust documentation, and adherence to model risk governance standards. Proactively identify model limitations and guide enhancements or redevelopment as needed. Maintain a strong control environment around model usage and results interpretation. Global Collaboration: Coordinate with Risk Analytics colleagues and other risk stripes globally to ensure consistency and best practices in risk modeling. Contribute to firm-wide risk initiatives and working groups, sharing expertise in credit risk analytics. Leverage the global footprint of the team by seamlessly integrating efforts between Mumbai, New York, London, and Budapest offices. Experience: Minimum 13 (preferable 15plus) years of experience in credit risk management, risk analytics or related quantitative finance fields, with a track record of increasing responsibility. Significant people management experience is required ( you should have successfully led high-performing teams and managed multi-disciplinary projects. Experience in a global financial institution or credit rating agency, particularly in roles involving credit risk modeling or analytics, is highly desirable. Technical Expertise: Deep understanding of credit risk modelling and quantitative techniques. Expert knowledge of probability of default (PD), loss given default (LGD), and exposure at default (EAD) modeling, credit scoring methodologies, and portfolio credit risk concepts. Familiarity with counterparty credit risk metrics (e.g., PFE, CVA) and structured credit products is a plus. Strong foundation in statistics, econometrics, and data analysis; proficiency in programming (Python, R or similar) for risk analytics is preferred. Ability to oversee the development of models employing techniques ranging from logistic regression to advanced machine learning, while ensuring they are conceptually sound and fit for purpose. Education: Advanced degree (Masters or PhD) in a quantitative discipline such as Finance, Economics, Statistics, Mathematics, Engineering, or a related field. A strong academic foundation in quantitative methods and risk management principles is essential. Regulatory Knowledge: Comprehensive understanding of global regulatory frameworks and guidelines for credit risk. This includes Basel II/III capital requirements (Foundation and Advanced IRB approaches), stress testing regimes (such as DFAST), and accounting standards for credit impairments (CECL and IFRS 9). Demonstrated experience interfacing with regulators or auditors on risk model issues, and familiarity with the expectations of model validation and governance processes. Leadership Skills: Exceptional leadership and organizational skills with the ability to build, inspire, and retain a high-caliber team. Proven capability in coaching and developing talent and fostering cross-team collaboration. Strategic thinker who can navigate complex problems and make decisions in high-pressure situations. A track record of driving change and innovation in a risk analytics or similar function, and championing initiatives from concept through execution. Ability to translate complex quantitative concepts and risk results into clear, concise insights for senior management, regulators, and non-technical stakeholders. Strong influencing skills (able to effectively advocate for model improvements, risk policy changes, or resource needs. Fluent in written and spoken English; confidence in articulating a vision and representing the team in executive forums. Industry Engagement (Plus): Active involvement in the wider risk management community is a plus. Professional certifications such as FRM (Financial Risk Manager), PRM, or CFA are advantageous. Keeping abreast of industry trends, such as developments in credit risk modeling techniques, fintech innovations, and emerging risks (e.g., climate risk),demonstrates the forward-looking mindset we value in this role. Experience with AI/ML applications in risk analytics or big data tools for risk management would be an added benefit Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren't just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There's also ample opportunity to move about the business for those who show passion and grit in their work. To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices into your browser. We work to provide a supportive and inclusive environment where all individuals can maximize their full potential. Our skilled and creative workforce is comprised of individuals drawn from a broad cross section of the global communities in which we operate and who reflect a variety of backgrounds, talents, perspectives, and experiences. Our strong commitment to a culture of inclusion is evident through our constant focus on recruiting, developing, and advancing individuals based on their skills and talents.

Responsibilities

Manage and mentor a diverse team of credit risk analytics professionals while driving the strategic development of the firm's credit risk measurement methodologies. Oversee the end-to-end lifecycle of credit risk models and ensure compliance with regulatory standards.