Finance Forecast Variance, Cash Runway, and Decision Review
Reconcile actuals, budget, and forecast; explain operating and accounting drivers; test cash runway scenarios; and produce evidence-linked actions, review gates, and executive decisions.
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Outcome: Analyze. Turn datasets, spreadsheets, experiments, and metrics into traceable findings and decisions. Clear Outcome
Reconcile actuals, budget, and forecast; explain operating and accounting drivers; test cash runway scenarios; and produce evidence-linked actions, review gates, and executive decisions.
Turn messy data quality findings into a prioritized remediation backlog with root causes, owners, validation checks, controls, and governance cadence.
Analyze spreadsheet KPI movement, reconcile numerator, denominator, volume, rate, mix, timing, and data effects, and produce evidence-qualified findings with concrete verification and decision gates.
Produce an evidence-linked assessment of a dataset, benchmark, leaderboard result, or research metric, including provenance, methodology, freshness, comparability, limitations, claim fidelity, and fit for the intended use.
Produce decision-linked KPI requirements, calculation-ready metric definitions, source assessments, executable data quality test specifications, dashboard recommendations, and an evidence-based build-readiness decision.
Assess whether an analysis can support a causal claim by examining identification assumptions, design-specific threats, diagnostics, and alternative explanations.
Produce an evidence-grounded experiment readout covering design validity, metric effects, statistical uncertainty, caveats, segment findings, and an approval-ready recommendation.
Turn dashboard goals, user decisions, KPI definitions, data sources, interactions, and governance constraints into an implementation-ready requirements specification.
Design an execution-ready SQL analysis plan covering data grain, tables, joins, metrics, filters, query structure, validation queries, risks, and acceptance evidence.
Audit a dataset for schema defects, missingness, duplicates, invalid values, outliers, join failures, privacy risks, and fitness for analysis.