Senior Forecasting & Business Analytics Consultant; AI

IWU is seeking a Senior Forecasting & Business Analytics Consultant (AI) (Independent Contractor ‑ 1099 / Contract-to-Hire) for a large-scale technology reorganization project.

• IWU is re-platforming its enterprise on a multi-data center,
  AI-first data foundation with a dedicated analytics planning, design, and build track on the roadmap. We need reporting that executives trust and forecasts that hold up under scrutiny. We are seeking a Senior Forecasting & Business Analytics Consultant with strong hands-on forecasting and actuarial modeling skills to lead IWU's forward-looking analytics functions.
• This is not a dashboard-only role. You will run a small analytics team
  , and the enterprise reporting portfolio, and stay hands-on in the models – cohort retention, tuition and aid scenarios, headcount and capacity forecasts, and multi-year financial projections that Finance, Leadership, and the CIO rely on.
• You will partner across IT, Institutional Research, Finance, and academic operations while standing up analytics capabilities on our on-premises data platform
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• You will be a people leader who still opens the spreadsheet, SQL editor, Python, or modeling tool when the forecast matters – you set standards for the team, quality-check the math, and explain variance to leadership in plain language.

• Drive the forward-looking analytics portfolio – executive dashboards, operational reports, self-service datasets, and KPI definitions aligned to IWU's strategic and re-platforming priorities.
• Develop the analytics workstream through planning, design, and build phases – requirements, data sourcing, semantic models, report catalog, and release governance per the re-platforming roadmap.
• Define reporting standards – naming, refresh cadence, data lineage documentation, access controls, and change management so reports do not become conflicting sources of truth.

• Develop and maintain enrollment and retention forecasts – cohort models, persistence curves, channel mix (online vs on-campus, graduate vs undergraduate), and scenario analysis leadership uses for planning.
• Build revenue and tuition forecasting models – net tuition, financial aid impact, discount rates, and program-level projections with documented assumptions and sensitivity analysis.
• Apply advanced statistical and forecasting methods – survival analysis for retention and persistence, cohort-based attrition modeling, confidence intervals, Monte Carlo simulations, scenario trees, and back-testing against actual enrollment, completion, and financial outcomes.
• Own the forecast cadence – monthly actuals vs forecast variance analysis, re-baseline triggers, and executive-ready narrative on what changed and why.

• Partner with Data Platform and DBA teams on our Single-Source-of-Truth and Data Warehouse – source integration, dimensional models, metric layers, and vector/analytics adjacency for AI-enabled reporting where standards apply.
• Design trusted datasets and semantic models – single definitions for headcount, enrollment, completion, revenue, and operational KPIs used across reports and forecasts.
• Implement data quality checks on reporting inputs – reconciliation to systems of record, anomaly detection, and escalation when source data breaks forecasts or dashboards.
• Align analytics delivery with identity and security standards – row-level security, FERPA-appropriate access, and audit trails for sensitive institutional data.

• Partner with Finance, Institutional Research, enrollment management, and academic leaders – translate their questions into models and reports they will actually use in decisions.
• Present forecast results and uncertainty honestly – ranges, assumptions, and risks – not false precision that collapses at the first bad semester.
• Support audit, accreditation, and compliance reporting – Title IV, FERPA, board materials, and SOX-adjacent controls where IT spend and headcount evidence is required.
• Document methodology and model governance – version control, assumption…