Intern, Software

About the Role

As an Intern, Software, you will transform business data into meaningful insights that support smarter decision-making across supply chain, manufacturing, and operations. You will contribute to analytics solutions that improve forecasting accuracy, inventory visibility, capacity planning, and operational performance, gaining hands-on experience with modern data platforms, business intelligence tools, and analytics technologies.

Key Responsibilities

• Support the development of data-driven solutions and analytics models for forecasting, inventory monitoring, capacity planning, and supply chain optimization.
• Build and maintain data pipelines, datasets, and analytical solutions using tools such as SQL, Databricks, PySpark, and related technologies.
• Create Power BI dashboards and reports to monitor trends, performance metrics, variances, and operational insights.
• Validate analytical outputs against business requirements, approved calculations, and historical data to ensure accuracy and reliability.
• Collaborate with supply chain, operations, and digital teams to identify opportunities where data and analytics can improve decision-making and business outcomes.
• Contribute to continuous improvement initiatives across the organization.

Requirements & Tech Stack

• Foundational knowledge of Python, SQL, and Microsoft Excel.
• Interest in data analytics and business intelligence.
• Strong analytical thinking, problem-solving skills, and attention to detail.
• Ability to work collaboratively in a team environment and communicate effectively with technical and non-technical stakeholders.
• Eagerness to learn new technologies and apply data-driven approaches to solve business challenges.

Qualifications

• Currently pursuing or recently completed a B.E., B.Tech., M.Tech., or M.Sc. in Computer Science, Information Technology, Data Science, Artificial Intelligence, or a related field.

Preferred Qualifications:

• Exposure to Power BI, Databricks, PySpark, ETL/ELT processes, time-series analysis, machine learning, or full-stack development.
• Previous academic projects, internships, or coursework involving analytics, reporting, or data engineering.