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Data Analyst

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Onsite

Data Analyst

ICICI Securities

Data Analyst

Skills

SQL QueriesPythonMicroservicesAmazon EC2Data AnalysisMachine LearningAIAWS

2 months ago

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Important information


Contract type:

Permanent

Location:

Navi Mumbai, Maharashtra, India

Work mode:

Onsite

Published on:

10 August 2026

What they need


We are seeking a highly motivated Data Scientist with 4-5 years of experience in data analysis, machine learning, and Generative AI. The ideal candidate should be comfortable working with large datasets, extracting business insights, building predictive models, and developing enterprise-grade Generative AI applications on AWS. The candidate should demonstrate strong ownership, be capable of independently driving projects from requirements to production and collaborate effectively with cross-functional teams.

Key Responsibilities

  • Analyse structured and unstructured data to derive actionable business insights.
  • Perform data cleaning, preprocessing, feature engineering, and exploratory data analysis (EDA).
  • Develop dashboards, reports, and data visualizations to support business decision-making.
  • Build, train, evaluate, and deploy machine learning models.
  • Design and implement Generative AI solutions using LLMs and Retrieval-Augmented Generation (RAG).
  • Develop scalable APIs and AI microservices using Python.
  • Integrate enterprise applications with AWS AI services.
  • Work with business stakeholders to understand requirements and translate them into analytical and AI-driven solutions.
  • Optimize model performance, prompt engineering, and retrieval pipelines.
  • Own end-to-end delivery, including development, testing, deployment, documentation, and production support.
  • Ensure code quality, security, and scalability following best engineering practices.

Required Technical Skills

1.SQL

2.Python

3.AWS service ( Bedrock, EC2, S3, Sagemaker, Transcribe)

4.Hands-on experience with Large Language Models (LLMs)

5.Prompt Engineering

6.Retrieval-Augmented Generation (RAG) effectively with cross-functional teams.

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