Data Analytics Internship – Capital Engineering

Opportunity Overview

Role Scope & Analytics Lifecycle

The Data Analytics Intern supports the end-to-end data processing workflow, transforming operational metrics into structured dashboards and predictive insights for engineering projects:

[Data Collection & Cleaning] ➔ [Exploratory Data Analysis (EDA)] ➔ [Dashboarding & Visualization] ➔ [Predictive Modeling Support]

Core Responsibilities

• Data Wrangling & QA: Collect, clean, and process raw datasets from varied sources while ensuring data quality and output accuracy.

• Exploratory Data Analysis (EDA): Perform EDA to identify operational trends, recurring patterns, and dataset anomalies.

• Dashboarding & Visualisation: Construct and maintain data visualisation dashboards to present key performance indicators (KPIs) to stakeholders.

• Statistical Modelling Support: Assist senior data analysts in running statistical tests and building predictive models for business requirements.

• Process Documentation & Reporting: Document analytical tasks, methodologies, and workflows; present progress updates and insights in team meetings.

Candidate Qualifications & Technical Matrix

Minimum Requirements

• Foundational Knowledge: Core understanding of data analysis principles, statistical concepts, and data hygiene.

• Problem-Solving Mindset: Strong analytical reasoning, critical thinking, and structured problem-solving skills.

• Communication: Ability to articulate technical findings clearly to team members and collaborate on cross-functional tasks.

Technical & Analytical Competency Matrix

Selection Workflow & Evaluation Pipeline

[Resume & Application Screening] ➔ [Analytical & Logic Assessment] ➔ [Technical Data Discussion] ➔ [Final Team Fit Interview]

1. Resume Screening: Evaluation of academic coursework, analytics projects, portfolio work, and basic data processing skills.

2. Analytical Assessment: Evaluation of logical reasoning, basic quantitative aptitude, and data interpretation abilities.

3. Technical Interview Loop: Scenario-based questions on data cleaning approaches, SQL/Python basics, and visualization techniques.

4. Final Culture Fit Round: Interaction with analytics leads to assess learning agility, communication clarity, and teamwork.