ICICI Lombard General Insurance - Data Scientist

Key Responsibilities:

- End-to-End Project Leadership: Take ownership of data science projects, from initial research and experimentation to scalable deployment, monitoring, and ongoing optimization.

- 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.

Core Technical Skills:

- Machine Learning & Statistics: Strong foundation in supervised & unsupervised ML algorithms, evaluation metrics, and feature engineering; practical experience with hands-on modelling and experimentation.

- LLM & Generative AI: Conceptual and practical understanding of LLM architecture, prompt engineering, RAG, parameter fine-tuning, and LLM evaluation/monitoring; exposure to vector databases; executed at least 1 impactful LLM/GenAI implementation in real-world settings.

- Programming: Proficiency in Python, R, and SQL for model development and data analytics.

- Deployment & Automation: Experience with cloud platforms (AWS/GCP/Azure); API integration and orchestration for scalable solutions; practical experience with MLOps tools like MLflow, Docker, or Kubernetes for CI/CD pipelines.

Soft Skills:

- Strong analytical thinking and structured problem-solving ability.

- Excellent interpersonal communication and stakeholder engagement.

- Ability to work in a fast-paced, collaborative environment and lead through influence.

- High ownership and drive to deliver measurable impact.

- Collaborate with product, engineering, and business stakeholders to translate complex AI solutions into business impact.

Educational Background:

- Bachelors/Masters degree in Computer Science, Data Science, Statistics, Mathematics.

- Equivalent industry experience with strong hands-on contributions will be preferred.

How to apply:

Interested candidates can share resume on with Data Scientist in the subject line along with the following details:

- Current Salary

- Expectation

- Notice Period

- Education (Full Time / Part time)