Role

Hire your AI Data Analyst

Dashboards, SQL, cohort analysis, experiment reporting — every metric interpreted, not just displayed.

Your AI Data Analyst turns event streams into decisions. Writes SQL against your warehouse, builds dashboards that update themselves, runs cohort and funnel analyses, ships experiment readouts. Every metric comes with interpretation — what it means, whether it's noise or signal, what to do next.

Hire your AI Data AnalystSee all roles
Free to startNo credit card requiredUpdated Apr 2026

What your AI Data Analyst does

01Write and maintain SQL queries against Postgres, Stripe Sigma, BigQuery, or Snowflake
02Build dashboards that answer specific questions, not vanity walls of charts
03Run cohort analysis on retention, revenue, and activation segmented by acquisition source
04Ship experiment readouts with statistical significance and business impact
05Investigate anomalies surfaced by the daily briefing — what caused the drop or spike
06Maintain the metric dictionary so every number across the company has one definition
07Build churn prediction and LTV models suited to your data volume
08Audit event instrumentation and flag broken or duplicated events
09Forecast MRR, cash burn, and growth with explicit confidence intervals
10Coordinate with AI CFO on revenue accuracy and AI CEO on weekly metric reviews

Workflows on autopilot

Daily metric delta
Every 6:30am, pulls yesterday's metrics vs. rolling 7-day and 28-day averages. Flags anomalies >15% off trend with a one-sentence hypothesis: 'Signups down 22% — correlates with the checkout 500 errors Linear flagged yesterday.'
Experiment readout
At experiment end, calculates statistical significance, confidence interval, and business impact (not just conversion lift, but projected MRR delta over 12 months). Flags secondary metrics that moved — the hidden side effects.
Cohort deep-dive
Monthly retention analysis by signup month, acquisition channel, plan tier, and any feature-flag cohort. Identifies what the best-retaining cohort did differently in week 1 vs. average cohorts.
Churn investigation
When the CFO flags a churn spike, pulls the list of churned customers, joins with usage data and support tickets, clusters reasons, and returns a ranked list: 50% price, 30% missing feature, 20% didn't activate.
Dashboard reduction
Quarterly audit of all dashboards. Kills the ones nobody looks at. Consolidates overlapping ones. Annotates every chart with the question it answers and what to do if the line moves.
Forecast refresh
Monthly re-forecast of MRR, cash, and headcount-cost (AI employees included). Shows three scenarios — base, bull, bear — with the drivers that move between them.

Without vs With a AI Data Analyst

Without
  • —You stare at PostHog for 40 minutes trying to remember how to query funnel drop-off
  • —Data team costs $180K/year, outputs 2 reports a month
  • —Experiment 'won' in PostHog — but it cost you LTV, you found out 6 months later
  • —Every teammate uses a different definition of 'active user'
  • —Dashboards multiply forever, nobody trusts any of them
With Tycoon
  • ✓Ask in chat, AI returns the query + chart + interpretation
  • ✓AI Analyst runs continuously, ships daily deltas, no OKR cycle
  • ✓AI checks secondary metrics on every experiment, flags hidden regressions
  • ✓Metric dictionary is the single source, AI enforces it
  • ✓Quarterly reduction, every chart has an owner and a question

A day in the life of your AI Data Analyst

06:30
Computes overnight metrics. Flags MRR up $340, signups down 12%, activation flat. Writes the delta narrative for the morning briefing.
08:30
Investigates the signup drop — runs SQL joining GA4 source with signup table. Finds Meta ad spend ran out at 11pm. Not a product issue.
10:00
Ships the experiment readout on the new onboarding: +18% activation (p<0.01), +11% day-7 retention, no secondary regression. Recommends full rollout.
12:30
Cohort analysis request from CEO — retention of December-signup cohort. Pulls and delivers in 20 minutes with annotated chart.
14:00
Updates the MRR forecast with March actuals. Burn rate on track, runway extended 6 weeks vs. last forecast.
16:00
Audits event instrumentation for the new feature. Finds 2 events double-firing, opens a Linear ticket for the CTO.
17:30
Ends day with the analysis log: 1 experiment shipped, 1 cohort delivered, 1 forecast refreshed, 2 anomalies explained.

Tools your AI Data Analyst uses

PostHog or Mixpanel for product analyticsStripe Sigma or Baremetrics for revenue analyticsGA4 and Fathom for traffic analyticsBigQuery or Snowflake for warehousingMetabase or Hex for dashboard buildingNotion or Linear for metric dictionary and analysis archivePostgres direct access via read replica for raw queriesGitHub for version-controlled SQL and dashboard configs
FAQ

Frequently asked questions

Clear answers about wallet credit, usage, subscriptions, and how Tycoon charges for work.

Do I need a data warehouse before hiring an AI Data Analyst?

No — the AI Data Analyst starts with whatever you have. Most one-person companies begin with just PostHog events, Stripe, and a Postgres read replica. The AI runs SQL against those directly. When data volume justifies it (usually past 50K events/day), the AI will flag that a warehouse would unlock deeper analysis and recommend BigQuery or Snowflake with a proposed ETL setup. Starting small avoids the 6-week warehouse setup project that kills the data initiative before it produces any insights.

Can it write SQL against my production database safely?

Only against a read replica or analytics-scoped user. Tycoon's default posture is read-only — the AI Data Analyst cannot write, update, or delete in production databases under any autonomy level. For any mutating query (flagging test users, backfilling a column), the AI drafts the migration, your AI CTO reviews, and a human approves the run. This is the same pattern a human analyst would follow on day one and never stops following. Medvi, for all their scale, runs this exact separation.

How does it handle stat significance and avoiding false wins?

Every experiment readout includes: effect size, p-value, confidence interval, required sample size vs. actual, and a check against 3 secondary metrics. If sample size is insufficient, the AI says 'inconclusive — need N more observations' rather than claiming a win. If p-value is marginal (0.05-0.10), it labels the result 'suggestive, not conclusive' and proposes the follow-up. This is how well-run data teams operate; the difference with Tycoon is consistency — no experiment gets 'shipped because it looks good' without the rigor check.

What if my metrics question is new or weird?

Ask in chat. 'How does LTV differ for customers who signed up from Product Hunt vs. organic?' becomes a SQL query, a chart, and an interpretation inside 20 minutes. If the data doesn't exist — say you want to segment by company size but don't collect company size — the AI tells you, proposes how to collect it (Clay enrichment on domain? onboarding question?), and drafts the implementation. Unlike dashboard tools that only answer pre-built questions, a real analyst answers ad-hoc questions in the language you asked them.

Will it coordinate with my finance and product decisions?

Yes — that's the point of Tycoon's multi-role architecture. The AI Data Analyst feeds the CFO's revenue forecasts with cohort LTV data, feeds the Product Manager's prioritization with usage patterns, and feeds the CEO's weekly strategy review with trend interpretation. Every analysis lives in the shared repo, so when the Product Manager asks 'which feature drives the highest retention?', the Analyst answers with the previously-built cohort model plus fresh data — not rebuilding from scratch. The institutional memory is what makes it feel like a real team, not a prompt with analytics access.

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Pillar

Run a One-Person Company With a Full AI Workforce (2026)

Build a one-person company with an AI workforce manager that gives you a CEO, CMO, CTO, COO, and operators. No hires, no freelancers — just you and an AI team.

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