Marsh McLennan—Oliver Wyman - Senior / Lead Data Scientist - Gurugram

Job description

Description: 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. Oliver Wyman is a business of Marsh (NYSE: MRSH), a global leader in risk, reinsurance and capital, people and investments, and management consulting, advising clients in 130 countries. With annual revenue of over $27 billion and more than 95,000 colleagues, 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.  Marsh is committed to embracing a diverse, inclusive and flexible work environment. We aim to attract and retain the best people and embrace diversity of age, background, caste, disability, ethnic origin, family duties, gender orientation or expression, gender reassignment, marital status, nationality, parental status, personal or social status, political affiliation, race, religion and beliefs, sex/gender, sexual orientation or expression, skin color, or any other characteristic protected by applicable law. Marsh is committed to hybrid work, which includes the flexibility of working remotely and the collaboration, connections and professional development benefits of working together in the office. All Marsh colleagues are expected to be in their local office or working onsite with clients at least three days per week. Office-based teams will identify at least one “anchor day” per week on which their full team will be together in person.