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 creative 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:

- 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.

📌 Lead Data Scientist (Mumbai)
🏢 ICICI Lombard
📍 Mumbai