Data analysis

Cleans messy tabular data (headers, types, duplicates, inconsistent categories, stray totals) and logs every change it makes. Use before analysing an export or a hand-maintained sheet.

Spreadsheet expert who builds clean, auditable Excel and Google Sheets workbooks, prefers simple formulas over clever ones and explains each step. Use as a standing spreadsheet helper.

Designs a pivot table that answers one specific business question and gives exact click-by-click setup steps for Excel or Google Sheets. Use when you have a flat table and a question about it.

Designs a tracker spreadsheet (projects, applications, habits, inventory, expenses) with dropdowns, conditional formatting, a summary tab and formulas. Use to organise recurring work in a sheet.

Analyses website analytics (GA4 or similar) for traffic sources, landing pages, engagement and conversion, flags tracking problems first and gives prioritised actions. Use as a marketer or site owner.

Classifies free-text records such as tickets, feedback or expenses into a given set of categories, with a confidence level and an explicit Other bucket, and returns a table.

Reconciles two datasets that should agree, such as bank versus ledger or CRM versus billing, by matching records, listing mismatches and explaining likely causes. Use for month-end checks.

Analyses quantitative survey responses with cleaning, tabulation, cross-tabs and optional weighting, and states the caveats about sample and response bias. Use before reporting survey numbers.

Analyses A/B test results with a sample-ratio-mismatch check, effect sizes, confidence intervals and guardrail metrics, ending in a ship, iterate or stop call. Use when an experiment ends.

Computes the sample size or statistical power for an experiment or survey, shows the formula and assumptions, and gives a sensitivity table. Use before launching an A/B test, study or survey.

Explains regression output in plain language (coefficients, intervals, p-values, fit) and what it does and does not let you conclude. Use when you have a model summary and need to explain it.

Consulting statistician who asks how the data were produced before analysing them, chooses methods that fit the question, checks assumptions and refuses to over-claim. Use for any data analysis.

Explains in plain words what a chart shows, what it does not show, how it might mislead and what to ask about it. Use when you are handed a chart in the news, a report or a meeting.

Recommends the chart that best carries a specific message for a given data shape, with encodings, the alternatives considered and the anti-patterns to avoid. Use before building a chart.

Critiques a chart for clarity, honesty (axes, scales, cherry-picked ranges) and accessibility, and proposes a concrete redesign. Use before a chart goes into a deck, report or dashboard.

Designs a readable data table for a report or slide, covering what to include, ordering, number formats, alignment, highlighting and footnotes. Use when your tables get skipped or misread.

Takes a stakeholder request from question to analysis plan, data checks, analysis and a decision-ready report, pausing for review between steps. Use when an analyst takes on a request.

Writes a precise metric definition (formula, grain, filters, edge cases, owner, known caveats) so every team computes the number the same way. Use when a metric is disputed or about to be launched.

Writes budget-versus-actual variance commentary covering material variances, drivers, timing versus permanent effects and forecast impact. Use as an FP&A analyst or budget holder at month end.

Writes a weekly business metrics update that explains movements against targets, the likely causes and the next actions. Use for the Monday update to leadership or the team channel.