Lead - Data Science

Actively Reviewing the Applications

ICICI Securities

India, Maharashtra, Navi Mumbai Full-Time On-site

Posted 23 hours ago • Apply by June 3, 2026

Job Description

Department: Data Science Unit

Key Responsibilities:

Design, develop, and deploy statistical and machine learning models to address business challenges across the BFSI sector.
Work on end-to-end model lifecycle- data exploration, feature engineering, model building, validation, and performance monitoring.
Leverage AWS AI/ML services, including hands-on work within the AWS Bedrock ecosystem, to build and scale AI solutions.
Experiment with and adopt Generative AI (GenAI) tools and frameworks to create innovative applications and business solutions.
Collaborate with cross-functional teams to translate business needs into technical requirements and actionable insights.
Stay updated with advancements in AI/ML and contribute to the continuous improvement of analytics capabilities.
Mentor junior team members and foster a culture of data-driven innovation.

Qualification:

Experience & Skills:

13 to18 years of experience in Analytics, Data Science, or AI, preferably within the BFSI domain.
Strong programming experience in Python with proficiency in libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch.
Hands-on experience with AWS AI stack (SageMaker, Bedrock, etc.).
Sound understanding of machine learning algorithms, statistical modeling, and data handling techniques.
Exposure to GenAI tools and frameworks and ability to explore new AI technologies.
Strong analytical and problem-solving skills with an ability to translate data insights into business actions.
Excellent communication, collaboration, and stakeholder management skills.
Demonstrated aptitude for learning and being a team player in a dynamic, fast-paced environment.
Prior experience working on AI-driven solutions in the BFSI sector.
Knowledge of LLM integration or prompt engineering for GenAI applications.
Experience with cloud-based model deployment and MLOps practices