Data Science Engineer

Job Description

RoleData Science (AI & Advanced Analytics)

Location:Pune

Shift9:30 AM to 6:30 PM.

Working days: 5 days a week

Role Summary

Work on data science, data analytics, and data-driven software quality use cases. Translate business questions into analytical concepts, data requirements, implementation tasks, and acceptance criteria, and deliver AI-enabled solutions and quick proofs of concept to business units.

Key Responsibilities

• Translate business questions and needs into analytical use cases, data requirements, implementation tasks, and measurable outcomes.
• Use Python and SQL for data extraction, transformation, analysis, modelling, validation, and insight generation on large datasets.
• Apply machine learning techniques such as clustering, decision trees, and artificial neural networks.
• Apply statistical methods: regression, statistical distributions, hypothesis testing, and model evaluation.
• Define data-quality methods, validation checks, transformation logic, and reliable metric definitions.
• Deliver vibe-coded Proofs of Concept (PoCs) to business units for initial feedback.
• Define frontend outputs: views, filters, comparison options, key metrics, and decision-support materials.
• Build GenAI-assisted workflows and design comparison logic, review workflows, and quality-evaluation methods for AI-generated outputs.
• Deliver AI use cases on AWS.
• Collaborate across the organization to identify data-driven solution opportunities.
• Explain data models, analytical methods, technical constraints, validation results, and business value to technical and non-technical stakeholders.

Minimum Qualifications

• BE / ME / MSc / BTech / MTech in Computer Science, IT, Statistics, or Mechanical Engineering.
• Experience in Data Science, Data Analytics, or data-driven software quality use cases.

Required Skills:

Core Data & Analytics: Strong Python and SQL; machine learning (clustering, decision trees, ANN); statistics (regression, distributions, hypothesis testing, model evaluation); data quality, validation, transformation logic, and metric definition.

AI & Advanced Analytics: GenAI-assisted workflows using n8n and UiPath; comparison logic, review workflows, and quality evaluation of AI-generated outputs.

AWS & AI: Amazon S3, AWS Glue, Amazon Athena, AWS Lambda, Amazon Bedrock.

Coding Assistants: Extensive use of AWS Kiro, GitHub Copilot, OpenAI Codex, and Claude Code.

Business & Communication: Defining dashboards and decision-support outputs; cross-functional collaboration and stakeholder management; clear communication for technical and non-technical audiences.