UPES School of Computer Science aims to be a data-centric institution where decisions are guided by measurable signals, not intuition alone. In this role, analytics becomes a strategic asset, helping the School:
- Anticipate student needs and improve success outcomes
- Understand employer trends and strengthen placement effectiveness
- Monitor and elevate faculty research performance
- Make real-time strategic decisions with confidence
Education
- Bachelor's degree in data science, Statistics, Computer Science, Analytics, Economics, or related field
- Master's degree preferred (Data Science, Business Analytics, Information Systems, Applied Statistics)
Experience
- 58+ years of experience in data analytics, business intelligence, or strategic insights roles
- Proven track record of delivering analytics solutions that influenced leadership decisions
Key Responsibility:
Dashboard & Reporting Architecture
- Design, develop, and maintain dynamic dashboards using tools such as Power BI, Tableau, Google Data Studio, and automation solutions
- Provide a daily executive dashboard for the Dean with key indicators such as KPIs, trends, alerts, and predictive signals.
- Promote self-service analytics frameworks that empower stakeholders to explore data independently.
- Ensure data accuracy, governance, version control, and performance tuning of all reporting systems
Predictive Analytics & Student Success Modelling
- Build predictive models to identify at-risk students, forecast attrition likelihood, and recommend intervention strategies.
- Collaborate with academic and student success teams to operationalize models into advising workflows and success interventions.
- Analyze correlations between academic metrics, student engagement, and success outcomes.
Tracking Placement Quality & Employer Trends
- Monitor placement metrics: offer acceptance, industry spread, salary distribution, role types, conversion ratios, and time-to-hire trends.
- Provide insights on alumni career trajectories, emerging job roles, and demand signals via labour market APIs and talent analytics platforms.
- Segment placement data by program, specialization, and demographic factors to identify strengths and opportunities.
Research Impact Auditing & Faculty Analytics
- Track and analyze faculty research impact metrics such as citations, h-index, publication growth, co-authorship networks, funding alignment, and global ranking signals.
- Develop faculty research dashboards and contribute to evidence for research portfolios, performance reviews, and strategic partnerships.
- Integrate bibliometric datasets (Scopus, Web of Science, Google Scholar) into automated reporting pipelines.
Insights for Strategic Decision-Making
- Translate complex datasets into executive-ready insights with actionable narrative interpretations.
- Identify patterns, anomalies, and strategic opportunities and communicate them clearly to the Dean, Associate Deans, and leadership committees.
- Support business cases for new initiatives (labs, industry programs, accreditation responses, strategic investments) with quantitative justification.
Data Governance & Quality Assurance
- Define and enforce data quality standards, naming conventions, and lineage tracking.
- Build metadata catalogs, master data definitions, and documentation to support analytics consistency.
- Ensure compliance with data privacy policies and ethical use of information.
Core Competencies:
Technical Expertise
- Advanced proficiency with analytics and visualization tools (Power BI, Tableau, R etc.)
- Experience with automation tools (MS Automate), ETL frameworks, and cloud databases
Analytical Reasoning
- Ability to design predictive models for student behavior, placement forecasts, and research performance indicators
Strategic Storytelling
- Comfortable turning numbers into insights that influence major decisions and strategic directions
Collaboration & Communication
- Effective at partnering with academic leaders, IT teams, student success professionals, and external stakeholders
Quality & Attention to Detail
- High standards for data accuracy, documentation, and analytic reproducibility
