About Oliver Wyman
At Oliver Wyman, a Marsh (NYSE: MRSH) business, we bring deep industry insight, bold innovation, and a collaborative approach that cuts through complexity to help organizations navigate their most defining transformative moments.
As a business of Marsh, we work alongside the world’s leading experts across risk, reinsurance and capital, people and investments, and management consulting. Together with Marsh Risk, Guy Carpenter, and Mercer, we help organizations build resilience and competitive advantages from every angle. With annual revenue over $24 billion and more than 90,000 colleagues in 130 countries, Marsh helps build the confidence to thrive through the power of perspective.
For more information, visit oliverwyman.com, or follow us on LinkedIn and X
About Data and Analytics (DNA) Practice
At Oliver Wyman Data and Analytics, we partner with clients to solve tough strategic business challenges with the power of analytics, technology, and industry expertise. Our India DNA team brings high-quality analytics and quantitative talent into global consulting engagements, delivering practical, client-ready solutions across financial services and other priority sectors.
Role Summary
We are looking for a Quantitative Modeling and Risk Analytics professional with strong quantitative, analytical, and communication skills. The role will focus on developing, implementing, and enhancing analytical models across credit risk, loss forecasting, provisioning, stress testing, capital, and related banking and financial-services use cases.
You will work with Oliver Wyman partners and client stakeholders to translate business questions into robust quantitative solutions, from data preparation and methodology design through implementation, performance assessment, documentation, and business use. This is a hands-on role suited for someone who combines technical depth with clear, practical communication.
Key Responsibilities
Build, enhance and independently validate models such as PD, LGD, EAD, IFRS 9/ECL, stress testing, scorecards, loss forecasting, capital, profitability, and other financial models.
Translate business and risk questions into well-defined analytical approaches, model specifications, and measurable outcomes.
Prepare and analyze complex datasets, conduct exploratory analysis, engineer features, and establish reproducible modeling datasets and workflows.
Apply appropriate statistical techniques, assumptions, calibration approaches, and performance measures using Python, SQL, SAS, or similar tools.
Maintain high standards of code quality, documentation, confidentiality, and delivery discipline.
Collaborate with risk, finance, technology, and business stakeholders to refine requirements, explain results, and support implementation.
Required Experience and Qualifications
3 to 8 years of experience in model development/validation experience in credit risk quantitative modelling (IRB, CECL, IFRS9, predictive modelling, forecasting models)
Awareness of Model risk Management framework(1LoD, 2LoD and 3LoD in model building activities). Exposure to model risk governance and related standards such as SR 11-7, E-23, CP6-22/SS1-23, including monitoring and issue remediation
Strong understanding of statistical modeling, regression, time series, classification, forecasting, segmentation, model calibration, and performance metrics
Experience in banking, financial services, consulting, analytics GCCs, risk, finance, or advanced-analytics teams.
Bachelor’s or master’s degree in Statistics, Mathematics, Economics, Finance, Engineering, Computer Science, Data Science, or another quantitative discipline.
Proficiency in programming language(Python and SQL); experience with SAS, R, Spark, or cloud-based analytics environments is an advantage.
Working knowledge of financial-services use cases such as credit risk, portfolio analytics, loss forecasting, provisioning, stress testing, or capital modeling.
Ability to produce clear technical documentation and explain model design, assumptions, results, and limitations to technical and business audiences.
Strong attention to detail, ownership mindset, and ability to manage deadlines in a fast-paced consulting environment.
What We Look For
Strong analytical judgment and comfort challenging model assumptions.
Practical problem-solving mindset with focus on business impact.
Clear written and verbal communication.
Ability to work independently while collaborating with global teams.
Curiosity, learning agility, and commitment to high-quality delivery.
Willingness to collaborate across time zones and travel when required.
Skills Required
- 3 to 8 years of experience in credit risk quantitative modeling, including IRB, CECL, IFRS 9, predictive modeling, or forecasting models
- Awareness of model risk management frameworks, including 1LoD, 2LoD, and 3LoD
- Exposure to model risk governance standards such as SR 11-7, E-23, CP6-22, or SS1-23, including monitoring and issue remediation
- Strong understanding of statistical modeling, regression, time series, classification, forecasting, segmentation, model calibration, and performance metrics
- Experience in banking, financial services, consulting, analytics GCCs, risk, finance, or advanced analytics teams
- Bachelor's or master's degree in Statistics, Mathematics, Economics, Finance, Engineering, Computer Science, Data Science, or another quantitative discipline
- Proficiency in Python and SQL
- Experience with SAS, R, Spark, or cloud-based analytics environments
- Working knowledge of credit risk, portfolio analytics, loss forecasting, provisioning, stress testing, or capital modeling
- Ability to produce technical documentation and explain model design, assumptions, results, and limitations to technical and business audiences
- Strong analytical judgment, attention to detail, ownership, problem-solving, communication, collaboration, and deadline-management skills
- Willingness to collaborate across time zones and travel when required
Marsh McLennan Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Marsh McLennan and has not been reviewed or approved by Marsh McLennan.
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Leave & Time Off Breadth — Leave offerings are described as generous, including sizable PTO, paid holidays, paid sick days, and additional time off such as paid volunteer time and “Summer days.” These time-off benefits are portrayed as a standout part of the overall rewards package.
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Healthcare Strength — Healthcare coverage is characterized as comprehensive, spanning medical, dental, and vision options, with additional supports like disability and life insurance and access to mental health resources and an EAP. The breadth of plan options is positioned as a core strength of the benefits package.
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Retirement Support — Retirement benefits are framed as solid, with 401(k) programs and employer matching frequently highlighted alongside other financial programs. Stock purchase options are also referenced as an additional wealth-building component of the total rewards mix.
Marsh McLennan Insights
What We Do
Marsh McLennan (NYSE: MMC) brings together nearly 78,000 experts in risk, strategy, and people across Marsh, Guy Carpenter, Mercer, and Oliver Wyman, serving clients in over 130 countries. Marsh enables enterprise worldwide by helping clients manage risks, transforming uncertainty into opportunity. Guy Carpenter helps clients grow profitably with reinsurance broking expertise, advisory services, and advanced analytics. Mercer helps organizations advance the health, wealth, and careers of their most vital asset — their people. Oliver Wyman’s expertise in strategy, operations, risk, and organization transformation changes what is possible for our clients, their industries, and society. Together, we combine a unique range of capabilities to help our clients solve problems, seize opportunities, and build lasting success in increasingly complex operating environments.
