Data Analyst

Key Responsibilities:

Data Analysis & Reporting

• Collect, clean, validate, and analyze data from multiple university systems.
• Develop and maintain institutional dashboards, scorecards, and management reports.
• Generate periodic and ad-hoc analytical reports for university leadership.
• Monitor key performance indicators (KPIs) related to admissions, academics, placements, student success, research, and operations.

Data Visualization & Storytelling

• Design interactive dashboards and visual reports using Tableau and other visualization tools.
• Present analytical findings through compelling visual narratives and executive summaries.
• Translate complex datasets into actionable business recommendations.

Statistical Analysis & Predictive Analytics

• Apply statistical techniques to identify trends, patterns, and performance drivers.
• Build predictive models related to student retention, enrollment forecasting, academic performance, placement outcomes, and operational planning.
• Conduct hypothesis testing, regression analysis, segmentation, and forecasting studies.

Data Management & Automation

• Develop automated reporting solutions using Python, R, SQL, Excel, and Google Sheets.
• Ensure data quality, consistency, and governance across institutional datasets.
• Create data pipelines and workflows to improve reporting efficiency.

Strategic Support

• Partner with academic and administrative departments to address data-related challenges.
• Support accreditation, ranking, regulatory, and compliance reporting requirements.
• Provide evidence-based recommendations for policy formulation and institutional improvement initiatives.

Required Technical Skills

Advanced

• Microsoft Excel (Advanced Functions, Pivot Tables, Power Query)
• Google Sheets (Advanced Formulas)
• SQL (Querying, Joins, Data Manipulation, Database Management)

Programming

• Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn)
• R Programming for Statistical Analysis

Visualization

• Tableau (Dashboard Development, Storytelling, KPI Monitoring)
• R - ggplot visualisation

Analytics & Statistics

• Descriptive and Inferential Statistics
• Hypothesis Testing
• Regression Analysis
• Predictive Modeling
• Time Series Forecasting
• Machine Learning Fundamentals

Additional Advantage

• Experience working with ERP, SIS, LMS, CRM, or Higher Education data systems
• Knowledge of AI/ML applications in education analytics
• Cloud Technologies - Azure, Data Bricks, Snowflake, Tableau Cloud

Required Qualifications

Essential

• B.Tech/B.E. in Computer Science Engineering (CSE), Artificial Intelligence (AI), Machine Learning (ML), Data Science, or related discipline
• Postgraduate Degree/Certifications in Analytics, Data Science, Statistics, Computer Applications, Management Analytics, Business Analytics, or related field

Preferred Certifications

• Tableau Certification
• Google Data Analytics Professional Certificate
• Microsoft Data Analyst Certification
• Python or Machine Learning Certifications

Preferred Experience

• Experience in analytics, consulting, IT services, business intelligence, education technology, BFSI, telecom, healthcare, or other data-intensive industries.
• Candidates from service-based organizations, analytics consulting firms, or business intelligence functions will be preferred.
• Prior experience in higher education analytics and Ranking/ Accreditation will be an added advantage.

Competencies

• Strong analytical and problem-solving skills
• Data storytelling and presentation capabilities
• Stakeholder management and communication skills
• Attention to detail and data accuracy
• Strategic thinking and decision support orientation
• Ability to work independently and manage multiple projects

Key Performance Indicators (KPIs)

• Accuracy and timeliness of reports
• Dashboard adoption by stakeholders
• Reduction in manual reporting effort through automation
• Quality of predictive models and forecasts
• Contribution to institutional decision-making
• Improvement in data governance and reporting standards