Oliver Wyman – Senior / Lead Data Scientist (AI and Generative AI) - Gurugram

AI in this role

langchainllamaindexhugging-facepytorchtensorflowscikit-learnmlflowdatabricks

prompt-engineeringragfine-tuningnlpai-safety

Company:

Oliver Wyman

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 an AI and Generative AI professional with strong data science, machine learning, software engineering, and communication skills. The role will focus on designing, developing, evaluating, and deploying practical AI / GenAI solutions across client use cases such as knowledge assistants, document intelligence, workflow automation, advanced analytics, decision support, and responsible AI governance.

You will work with Oliver Wyman partners, consultants, and client stakeholders to translate business problems into AI-enabled solutions, build prototypes and reusable assets, evaluate model quality and risks, and communicate technical findings in a clear, client-ready manner. This is a hands-on role suited for someone who can combine technical depth with practical business thinking.

Key Responsibilities

Develop AI, machine learning, and GenAI solutions using Python, SQL, cloud platforms, LLM APIs, open-source models, and modern AI frameworks.
Translate client business problems into analytical approaches, prototype designs, solution requirements, and scalable implementation plans.
Build and evaluate GenAI applications such as retrieval-augmented generation knowledge assistants, document summarization and extraction tools, workflow copilots, conversational agents, semantic search, and prompt-driven analytics.
Work with structured and unstructured data, including text, documents, images, transcripts, logs, and enterprise knowledge sources.
Support model experimentation, prompt engineering, embedding design, retrieval strategy, fine-tuning / adaptation approaches, benchmarking, and output quality assessment.
Apply evaluation techniques covering relevance, accuracy, robustness, hallucination risk, bias / fairness, explainability, human-in-the-loop review, and business impact metrics.
Build clean, reliable code, notebooks, APIs, pipelines, demos, and reusable analytics assets following engineering and documentation best practices.
Collaborate with consultants, data engineers, designers, risk / governance teams, and client subject matter experts to refine requirements and deliver client-ready outputs.
Support responsible AI considerations, including privacy, security, data lineage, model limitations, auditability, compliance, and safe deployment.
Develop clear documentation, technical findings, user guides, issue logs, and practical recommendations for technical and business audiences.

Required Experience and Qualifications

3 to 8 years of experience in AI / ML, data science, GenAI engineering, NLP, advanced analytics, software engineering, or related consulting / analytics roles.
Experience developing ML models, AI applications, LLM-powered workflows, analytics products, or data-driven decision tools in consulting, financial services, analytics GCCs, technology firms, startups, or enterprise teams.
Bachelor's or master's degree in Computer Science, Data Science, Engineering, Statistics, Mathematics, Economics, AI / ML, or another quantitative or technical discipline.
Strong hands-on experience with Python and SQL; experience with PyTorch, TensorFlow, scikit-learn, Spark, or cloud-based analytics environments is an advantage.
Working knowledge of GenAI concepts such as LLMs, transformers, embeddings, prompt engineering, RAG, agents, fine-tuning, evaluation, and guardrails.
Understanding of ML fundamentals, including feature engineering, supervised and unsupervised learning, model validation, performance metrics, experimentation, and deployment lifecycle.
Experience handling unstructured data and building data preparation, transformation, or retrieval pipelines.
Ability to write clear technical documentation and explain AI / GenAI outputs, limitations, and trade-offs to both technical and business audiences.
Strong attention to detail, ownership mindset, and ability to manage deadlines in a fast-paced consulting environment.

Preferred / Valued Experience

Exposure to enterprise AI / GenAI use cases across financial services, insurance, risk, finance, operations, customer service, knowledge management, or productivity transformation.
Experience with LangChain, LlamaIndex, Hugging Face, vector databases, MLflow, Databricks, Snowflake, Airflow, Docker, APIs, or CI / CD practices.
Familiarity with cloud AI services and platforms such as Azure, AWS, or Google Cloud.
Exposure to responsible AI, model governance, model risk management, privacy reviews, security controls, compliance expectations, or audit-ready documentation.
Consulting experience or experience in client-facing analytics, data science, product, technology, or transformation roles.
Experience supporting adoption of AI solutions, including user testing, training materials, change management, and production rollout support.

What We Look For

Hands-on builder mindset with curiosity for emerging AI and GenAI techniques.
Practical problem-solving orientation with focus on business impact.
Clear written and verbal communication.
Ability to work independently while collaborating with global teams.
Strong learning agility, delivery discipline, and commitment to high-quality work.
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.

How we rate this

Oliver Wyman – Senior / Lead Data Scientist (AI and Generative AI) - Gurugram at Marsh McLennan rates 88 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Prompt EngineeringRAGFine TuningNLPAI SafetyLangChainLlamaIndexHugging Face

Questions you could be asked

How do you structure and test a prompt to get consistent output from a language model?
How would you design a retrieval step so the model answers from real data instead of guessing?
Walk me through fine-tuning a model: what data did you use, and how did you check the result?
What NLP problem have you worked on, and how did you measure whether it actually worked?
How do you think about the risk of an AI system in this kind of role failing silently?

Adapt your resume

List these exact terms on your resume: Prompt Engineering, RAG, Fine Tuning, NLP, and AI Safety. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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