Lead Data Scientist

ICICI Lombard; Mumbai City; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

ICICI Lombard is seeking a Senior Data Science Lead in Mumbai to own data science projects from research to deployment and monitoring. You will define a data science roadmap, influence senior leadership, and mentor a high-performing team of data scientists and analysts.

You will drive governance, scaling, and cross-functional collaboration, delivering innovative GenAI solutions with a strong emphasis on responsible AI and cloud-native pipelines.

Qualifications

• Strong foundation in supervised & unsupervised ML algorithms, evaluation metrics, and feature engineering.
• Hands-on modelling and experimentation experience.
• Experience leading data science teams and cross-functional collaboration.

Responsibilities

• Own data science projects from research to scalable deployment and monitoring.
• Define data science roadmap aligned with business priorities and influence leadership.
• Lead and grow a high-performing data science team.
• Set best practices for responsible AI and scale analytics across functions.
• Design and optimize ML models to solve business problems.
• Research and implement GenAI solutions including prompt engineering and RAG.
• Collaborate with product/engineering to translate challenges into solutions.
• Mentor junior data scientists and analysts.
• Design scalable data models and automated ML pipelines with MLOps in the cloud.
• Implement monitoring to track deployed models' performance.

Skills

ML fundamentals

Hands-on modelling

LLM & GenAI

Vector databases

Real-world GenAI

Python, R, SQL

Cloud platforms

API integration

MLOps (MLflow, Docker, Kubernetes)

Tools

MLflow

Docker

Kubernetes

Job description

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 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.

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.