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.