Data Scientist

Job Summary

Position: Data Scientist

Notice Period: Immediate Joiner

Experience: 5+ Years

Skills:eda, machine learning, gen ai, data scientist, python, ml, sql, generative ai, llm, models, ai/ml, deep learning

Key Responsibilities

• Collect, clean, and preprocess data from diverse sources to ensure quality and accuracy.
• Develop and refine statistical models and Machine Learning (ML) algorithms to solve real-world problems.
• Collaborate with cross-functional teams (e.g., data engineering, product, and business) to identify and understand analytical needs.
• Perform exploratory data analysis (EDA) to uncover patterns, trends, and relationships in datasets.
• Evaluate model performance using appropriate metrics, and iterate to optimize accuracy and efficiency.
• Document analyses, methodologies, and best practices to maintain clear records for future reference.
• Stay up-to-date with emerging AI/ML technologies and actively explore new approaches—especially in the areas of deep learning, Generative AI, and Large Language Models (LLMs).
• Present findings and insights to both technical and non-technical stakeholders in a clear, concise manner.

Qualifications

• Design and Build Data Pipelines: Assist in the development of reliable, efficient ETL (Extract, Transform, Load) and/or ELT pipelines that move data from a variety of internal and external sources into data storage systems.
• Data Integration and Collaboration: Work closely with data scientists, data analysts, and software engineers to ensure data is readily available and properly structured for analysis and modeling.
• Data Quality and Validation: Implement quality checks, error handling, and monitoring processes to ensure the reliability and accuracy of data.
• Optimize Performance: Contribute to the optimization of data pipelines and database queries to handle high-volume data and achieve better performance and scalability.
• Maintain Documentation: Develop and maintain technical documentation for data systems, pipelines, and processes to keep the team aligned and informed.
• Security and Compliance: Support efforts to ensure secure data handling and compliance with relevant regulations and industry best practices.
• Continuous Learning and Innovation: Stay informed about emerging data engineering tools, techniques, and technologies; share learnings with the team; and implement improvements where applicable.