Role &

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 cutting-edge 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.

Preferred candidate profile :

- 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 :

1.

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.

📌 ICICI Lombard - Lead Data Scientist (India)
🏢 ICICI Lombard
📍 India