The setting

Cordova Bikes sells electric commuter bikes direct to consumers online, plus one showroom in Portland. You are the sole financial analyst, reporting to CFO Elena Marquez, and the company runs about 1.7 million dollars a month in revenue.

Every mission on this path happens at the same company, so context carries over the way it does in a real job: the data you cleaned in mission two is the data the finance lead questions in mission four.

The missions

1. data prepstarter

Clean the Q2 sales export before Cordova's close

Elena needs a clean revenue view for the Q2 close, and the raw export from the order system is a mess. You cannot analyze anything until the data is trustworthy.

You deliver: A cleaned Q2 sales table plus a data quality log naming every issue by order id and how it was resolved.

Scored on: Catches the real issues, Standardizes product names, Documented fix rules, Usable cleaned table.

Working from: raw_q2_sales.csv, data_notes.md.

2. variance analysisstarter

Explain Cordova's Q2 budget miss to the CFO

Q2 came in behind plan and Elena wants a one-page variance memo before the leadership sync. She does not want a table of every line, she wants to know what actually drove the miss.

You deliver: A one-page variance memo naming the driver line item with dollar and percent variance and a favorable or unfavorable call on each line.

Scored on: Correct variance math, Identifies the driver, Revenue context, Clear read for the CFO.

Working from: budget_vs_actual.csv.

3. forecastcore

Forecast Cordova's revenue for the next quarter

The board wants a revenue outlook for April through June 2026, and Elena wants your number, not a gut call. Cordova's demand has a clear shape: it climbs into summer and spikes for the holidays, then dips in winter.

You deliver: A three-month revenue forecast for April to June 2026 with the method, growth rate, and seasonal assumptions written out.

Scored on: Uses the real trend, Applies seasonality, Stated method, Names a risk.

Working from: monthly_revenue.csv.

4. unit economicscore

Prove which Cordova channel is losing money on every sale

Growth lead Devon Park wants to double the paid budget. Elena wants to know if the current spend even pays for itself before anyone adds a dollar. That answer starts with unit economics.

You deliver: A unit economics analysis with per-bike contribution margin, CAC by channel, and a verdict on which channels lose money on the first sale.

Scored on: Correct contribution margin, CAC by channel, Flags the loss channel, Clear verdict.

Working from: unit_costs.md, marketing_spend.csv.

5. board summarystretch

Write Cordova's Q2 board summary and the ask

The board meets Thursday. Elena wants one page that tells the true story of the quarter and ends with clear asks, not a wall of charts. You have the KPI scorecard and Elena's raw note on what the board cares about.

You deliver: A one-page board summary tying the KPIs into one story and ending with three ranked, quantified asks.

Scored on: Grounded in the KPIs, Connected narrative, Three ranked asks, Honest about the miss.

Working from: kpi_summary.csv, ceo_note.md.

How the scoring works

Each deliverable is graded against the rubric written for that mission. Separately, every mission on every path is graded on how you used AI, against the same four criteria:

  • Understood the task. The learner framed the goal for the assistant clearly instead of pasting the brief and hoping.
  • Grounded in the material. The learner directed the assistant into the provided files and based the work on them, not on invented facts.
  • Verified the output. The learner checked claims, numbers, or coverage against the source material before submitting.
  • Iterated with judgment. The learner refined weak parts of the draft with specific follow-ups rather than accepting the first answer.

Both scores, with the work behind them, go on your proof profile. That is what makes a claim like "I can use AI for finance and fp&a" something an employer can check.