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