Employment Type: Full-time

We’re seeking someone to join our team as a Director in Risk Analytics (IRB Credit Rating Model) team

Firm Risk Management

In the Firm Risk Management division, we advise businesses across the Firm on risk mitigation strategies, develop tools to analyze and monitor risks and lead key regulatory initiatives.

Company Profile

Morgan Stanley is an industry leader in financial services, known for mobilizing capital to help governments, corporations, institutions, and individuals around the world achieve their financial goals.

Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world.

What you’ll do in the role:

Responsibilities
•  Quickly develop a deep understanding of Morgan Stanley’s credit risk analytics models.
•  Participate in research, development, and implementation of credit risk models
•  Perform econometric analyses to support methodology development
•  Support backtesting, stress testing, scenario analyses and sensitivity studies
•  Analyze model changes and perform data analyses for various purposes including model improvement
•  Partner with teams across Risk Analytics, technology, model risk management, credit risk officers and other teams throughout FRM and the Firm.
• Own modeling efforts for credit risk modelling in Mumbai

What you’ll bring to the role:

Skills required (required / preferred)
• 0-10 years of work experience in quantitative modeling, Risk Management, algorithmic trading, global markets or any other quantitative/Data Science field.
• Prior analytics work experience in a bank credit-related department. Examples include lending or trading analytics.
• The candidate needs to be familiar with statistical techniques viz. Regressions Analysis, Hypothesis testing et al.
• Understanding of financial institutions regulatory frameworks. Examples include IRB, CECL, CCAR, Dodd-Frank and Basel.
• Strong quantitative and analytical skills and ability to work with diverse cultures in a global team.
• Knowledge and hands-on experience in one of the programming languages R, Python, MATLAB, SQL, C# or C++ is strongly preferred.
• Excellent communication skills (Oral and written). Ability to communicate and present logically, precisely and in simple manner, complex and technical issues.
• Attention to details and ability to work under pressure and cope with a fast moving environment.
• PRM/FRM, CFA, CQF certification is an advantage.
• Experience in AI, ML, NLP, Big Data Analytics, PowerBI is an advantage.

Candidate must have relevant experience in in statistical / mathematical modeling, quantitative research, counterparty and market risk management, or related field at a reputed bank, investment or broker services, asset management firm or a consulting firm. Wider skill requirements include:

• Independently built and managed quantitative market and counterparty risk analytical models
• Strong experience/knowledge in at least some of the following areas (in quant space)
• Counterparty Credit Risk (PFE, CVA, XVA)
• Pricing and valuation – Derivatives (across one or more asset classes)
• Modeling of Risk Metrics (e.g, EPE, PFE, RWA, Greeks)
• Market Risk Scenarios and Stress Testing
• Development, prototyping and back-testing of Monte Carlo Credit Exposure Models o Incremental default risk, specific risk charge and stressed VaR o Worked on multiple Market Risk Models like to develop/review calculation of VaR(Historical, Parametric and Monte Carlo), RNiV, CCAR, IRC Model Validation/ development and present value for various type of instruments using any statistical tool
• Strong experience/knowledge in at least some of the following areas (business knowledge)
• Good knowledge of market risk concepts: Risk Factor, VAR, Earning at Risk, cash flow at risk, ETL, PV01, Independent Validation, Exotic derivatives, FX, Interest rate derivatives, volatility, commodities, credit derivatives, Fixed income, Hull & White, Monte Carlo simulation, Capital calculations
• Knowledge and experience with counterparty risk concepts (PFE,SA-CCR, EPE etc
• Leveraging experiential know-how of a wide range of financial products like Equity, Derivative, Swaps, IR, Credit derivatives, OTC products, Swaps, Securitization, CDO’s etc.
• Knowledge of one or more of global regulatory Topics BASEL II/III, IFRS 9, CCAR/DFAST, CECL, FRTB, SR-11/7 around data sufficiency, modeling methods, industry standards etc.
• Assisted clients to design and implement strategic and functional changes across risk management, treasury, front office, middle office, and back office activities with a focus on risk and valuation processes, regulatory compliance, analytics, strategy, and organizational structure.
• Programming and Algorithms: R, Python, SAS, Matlab, Scala, VBA etc.
• Experience with with Murex, QRM, Reuters, FINCAD, Bloomberg and Algo is a plus

Non-functional skill requirements:

In order to succeed in PwC Risk CoE, it is desirable for candidates to possess:

• Understanding of market trends and demands in the financial services sector and issues faced by clients by staying abreast of current business and industry trends relevant to the client’s business
• Excellent oral and written communication skills
• Solid analytical and problem-solving skills; ability to isolate and solve issues using large amounts of data
• Process orientation with strong technical skills and attention to detail
• Deep technical capabilities and industry knowledge of financial products
• Willingness to travel to meet client needs, as needed

Educational Background:

Desired candidate must have a master’s degree or higher in a quantitative discipline such as Economics, Statistics, Mathematics, Operation Research, Econometrics, Data Science, Finance, Engineering + MBA; advanced degree is a plus; Industry relevant certifications in CQF, FRM, CFA, CPA certification is a plus

Job Responsibilities

• Lead the design, implementation, and enhancement of quantitative data quality frameworks, encompassing statistical validation and anomaly detection.
• Develop AI/ML-driven predictive quality checks, enabling proactive data error prevention and model trustworthiness.
• Apply advanced statistical methodologies — linear/non-linear modeling, time series analysis, and Bayesian inference — to detect quality drifts and signal inconsistencies.
• Collaborate with quantitative researchers, data scientists, and engineers to ensure data readiness for quantitative models and investment algorithms.
• Create automated, scalable, and auditable data validation pipelines, supporting real-time data monitoring and exception reporting.
• Partner with stakeholders to uphold data governance, privacy, and regulatory compliance standards (MiFID, ESMA, SEC).
• Mentor and guide junior analysts, fostering a culture of excellence, continuous learning, and innovation in quantitative analysis.
• Communicate complex data quality insights and statistical findings in simple terms to senior leadership and non-technical stakeholders.
• Drive innovation through automation, reproducible modeling pipelines, and deployment of ML-based data correction systems.
• Contribute to the modernization of Morningstar’s data architecture by integrating data observability, telemetry, and metadata-driven quality measures.

Requirements

• Strong foundation in quantitative finance, econometrics, and applied statistics.
• Deep understanding of financial instruments, fund structures, and performance modeling.
• Proven ability to work with large-scale, structured and unstructured data.
• Excellent analytical, problem-solving, and statistical reasoning skills.
• Strong stakeholder management, communication, and presentation skills.
• Ability to work in a cross-functional, fast-paced environment, and lead through influence.

Desired Candidate Profile

• Master’s degree in Statistics, Mathematics, Financial Engineering, Data Science, or Quantitative Finance.
• Professional certifications such as CFA, FRM, CQF, or Six Sigma Black Belt preferred.
• 10+ years of experience in quantitative analytics, model validation, or data quality engineering within financial services, asset management, or fintech.
• Expertise in Python, R, SQL, and familiarity with tools such as MATLAB, SAS, or TensorFlow.
• Experience in AWS ecosystem (S3, RDS, Glue, Athena) and modern data quality platforms.
• Hands-on experience with AI/ML frameworks (scikit-learn, PyTorch, TensorFlow) for anomaly detection and predictive data correction.
• Familiarity with data governance and regulatory standards (GDPR, SEC, ESMA, MiFID).
• Proficiency in Lean, Agile, and automation-first approaches for process improvement.
• Entrepreneurial mindset with a passion for innovation and scalability.
• Strong leadership, mentorship, and collaboration abilities.
• Flexible to adapt to evolving data and technology landscapes.

Key Responsibilities

• Develop, validate, and document credit risk models (PD, LGD, EAD) for retail and wholesale portfolios across regulatory (CECL, IFRS 9, Basel) and business-use contexts.
• Apply AI/ML algorithms (e.g., LightGBM, XGBoost, Random Forest, Neural Networks) to improve prediction power and model performance beyond traditional approaches.
• Implement Generative AI and LLM-based applications using RAG pipelines, document intelligence, and model documentation automation. Experience with agentic frameworks like Autogen, LangChain, or similar would be helpful.
• Experience of development and deployment of models in cloud-based platforms such as Azure, AWS, GCP etc.
• Design explainable AI solutions by incorporating techniques like SHAP, LIME, and feature attribution methods to enhance transparency in high-stakes modeling environments.
• Partner with cross-functional teams, including business stakeholders, technology teams, and model governance, to ensure model alignment with business objectives and regulatory expectations.
• Contribute to innovation initiatives and support proposal development, thought leadership, and solution architecture in the AI/ML space.

Required Skills & Experience

• 2–6 years of total experience, with minimum 2 years in AI/ML or GenAI model development or validation.
• Strong understanding of credit risk modeling frameworks, scorecard development, and risk metrics (e.g., RWA, Expected Loss, Economic Capital).
• Proficient in Python and SQL, with hands-on experience using ML libraries such as scikit-learn, Tensorflow, Pytorch and transformer-based LLM packages
• Familiarity with regulatory standards such as CECL, IFRS 9, CCAR/DFAST, Basel II/III, SR 11-7, and model governance best practices.
• Exposure to cloud environments (Azure preferred), version control (Git), and workflow automation tools.
• Experience with credit bureau data, vendor models (e.g., FICO, Moody’s, S&P), and financial benchmarking is a plus.
• Ability to clearly communicate complex technical content to non-technical stakeholders through reports, dashboards, and presentations.

Education & Certifications:

• Master’s degree or higher in Statistics, Mathematics, Economics, Data Science, Engineering, or Finance.
• Professional certifications such as FRM, CFA, CQF, or in product management equivalent are preferred.
• Contributions to opensource AI / ML projects and competitions is preferred