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, Num Py, 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