ICICI Lombard General Insurance - Lead Data Scientist - Data Science & Analytics

About ICICI Lombard:
ICICI Lombard is a leading insurance company known for its digital prowess and innovative use of technology.
The company embraces digital transformation and employs cutting-edge technologies such as AI, ML, and big data analytics to provide exceptional insurance experiences. By investing in advanced digital platforms and tools, ICICI Lombard simplifies insurance processes, facilitates customer interactions, and offers user-friendly mobile apps and web portals for policy purchase, claims filing, and self-service options.
ICICI Lombard remains at the forefront of the digital landscape by actively exploring emerging technologies to unlock new possibilities in the insurance industry. By harnessing data and analytics, the company develops personalized insurance solutions and effectively manages risks. Internally, ICICI Lombard optimizes operations, automates processes, and encourages collaboration through a tech-driven approach.
With its unwavering commitment to digital innovation and technological advancements, ICICI Lombard is poised to transform the insurance industry and redefine insurance service delivery, establishing itself as a digital insurance leader.
About the Role:
We are seeking an accomplished Lead Data Scientist to join our Data Science & Analytics team in Mumbai, where you will spearhead the end-to-end development and deployment of data-driven solutions from problem definition and model design to production implementation and monitoring while leveraging your expertise in statistics, machine learning, and Generative AI to deliver significant business impact; in addition to driving technical excellence, you will lead and mentor a team of data scientists, foster a culture of innovation and collaboration, and play a strategic role in scaling advanced analytics across the organization in a fast-paced, dynamic environment.
Responsibilities:
- End-to-End Project Leadership: Take ownership of data science projects, from initial research and experimentation to scalable deployment, monitoring, and ongoing optimization.
- Strategic Leadership: Define the data science roadmap, align initiatives with business priorities, and influence senior leadership through data-driven insights.
- Team & Culture Building: Lead, grow, and inspire a high-performing team of data scientists, fostering innovation, collaboration, and continuous learning.
- Governance & Scaling: Establish best practices for responsible AI, ensure compliance with data regulations, and scale advanced analytics solutions across functions and geographies.

- Model Development: Design, build, and optimize advanced statistical and machine learning models to solve complex business problems.
- LLM & Generative AI: Research and implement innovative LLM/GenAI solutions, including advanced prompt engineering, Retrieval-Augmented Generation (RAG) frameworks, and parameter-efficient fine-tuning for specific business needs.
- Cross-functional Collaboration: Partner with product, engineering, and business stakeholders to translate complex challenges into actionable data science solutions and communicate findings effectively to technical and non-technical audiences.
- Mentorship: Guide and mentor junior data scientists and analysts, fostering a culture of technical excellence and continuous learning.
- Infrastructure & Deployment: Work with data engineering teams to design scalable data models and robust, automated ML pipelines using MLOps best practices and cloud services (AWS/GCP/Azure).
- Performance Monitoring: Implement monitoring frameworks to track and enhance the performance of deployed models.
Educational Qualifications:
- Engineering Graduate (Full time)
Competencies Required:
- Machine Learning & Statistics:
1. Strong foundation in supervised & unsupervised ML algorithms, evaluation metrics, and feature engineering.
2. Practical experience with hands-on modelling and experimentation.
- LLM & Generative AI:
1. Conceptual and practical understanding of LLM architecture, prompt engineering, RAG, parameter fine-tuning, and LLM evaluation/monitoring.
2. Exposure to vector databases.
3. Executed at least 1 impactful LLM/GenAI implementation in real-world settings.
- Programming:
1. Proficiency in Python, R, and SQL for model development and data analytics.
- Deployment & Automation:
1. Experience with cloud platforms (AWS/GCP/Azure).
2. API integration and orchestration for scalable solutions.
3. Practical experience with MLOps tools like MLflow, Docker, or Kubernetes for CI/CD pipelines.
Behaviour Required:
- OneIL1Team:
1. Growth Mindset
2. Team Player
3. Adaptability