We are seeking a highly analytical and hands-on Data Scientist to join a team building data-driven personalization solutions while helping drive the organization''s transition into Generative AI and Agentic AI. This role requires someone who can work across the complete data science lifecycle—from understanding and validating large-scale datasets to building production-ready machine learning solutions and contributing to next-generation AI initiatives.

The ideal candidate has strong expertise in Python, SQL, and PySpark, enjoys solving business problems through data, and can communicate insights effectively to stakeholders. Experience with Google Cloud Platform, modern data pipelines, and LLMs/Agentic AI will be a significant advantage.

Key Responsibilities

Data Analysis & Business Insights

·                     Analyze large-scale structured and unstructured datasets including customer, transaction, and marketing data.

• Develop a deep understanding of data to identify trends, anomalies, and business opportunities.
• Translate analytical findings into actionable recommendations for business stakeholders.
• Present insights clearly to both technical and non-technical audiences.

Data Engineering & Pipeline Development

·                     Develop and maintain scalable data pipelines on Google Cloud Platform (Google Cloud Platform).

• Extract, transform, validate, and process data from multiple enterprise data sources.
• Build reliable, production-grade ETL/ELT workflows using PySpark and SQL.
• Monitor and optimize data pipelines for performance, scalability, and reliability.

Machine Learning

·                     Build, train, evaluate, and deploy machine learning models.

• Apply supervised and unsupervised learning techniques to solve business problems.
• Implement end-to-end machine learning workflows from data preparation through model deployment and monitoring.
• Continuously improve model performance using appropriate evaluation techniques.

Data Validation & Governance

·                     Design robust data validation frameworks to ensure data quality.

• Identify and resolve data quality issues and data drift.
• Ensure compliance with enterprise data governance and privacy standards.
• Implement automated validation and monitoring processes across data pipelines.

Generative AI & Agentic AI

·                     Contribute to AI initiatives involving Large Language Models (LLMs) and Agentic AI.

• Assist in designing and implementing AI-powered solutions using prompt engineering techniques.
• Evaluate opportunities to leverage transformers and modern AI frameworks for business use cases.
• Collaborate on enterprise AI solutions that integrate traditional machine learning with Generative AI capabilities.