Practice
Workplace simulations and interview practice. Optional beyond the weekday lessons; studios embed selected scenarios. Everything simulated is labelled.
- 100scenarios
- 25capabilities covered
- 62 hof simulated work
- 150interview questions
Scenarios by capability100
Production incident
Senior code review
Stakeholder simulation
Messy real-world data
ADR centre
Live coding and SQL
- SQL gym: rolling and same-weekday features with window functions30 min
- Python gym: entity-safe lag and rolling features in pandas30 min
- SQL gym: consecutive nearly-full streaks and why the first query lies35 min
- Python gym: turning cumulative booking curves into pickup in pandas35 min
- SQL gym: booking pace against the same weekday last year40 min
ML system design
Ambiguous business case
Verbal defense
Statistical traps
Ablations
Decision theory and value
Robustness and stress tests
Paper reproduction
RAG evaluation workbench
LLM failure lab
- Unsupported citations and broken JSON in a claims assistant35 min
- Injected instructions and a looping helpdesk agent40 min
- A claims summariser that never saw its last document35 min
- A refund assistant that paid twice, in the wrong unit35 min
- An oversized voucher late in a long baggage-claim conversation40 min
Multi-agent company
End-to-end lifecycle
Golden test set and regression
Hiring committee
AI cost–quality
Agents vs workflows
Teach-back and Socratic interviewer
Executive presentation
Research reading protocol
- Read a churn paper before believing its abstract35 min
- Read an agent benchmark paper before adopting its framework35 min
- Does long context make retrieval obsolete? Read the claim40 min
- A language-model judge that agrees with humans 85% of the time35 min
- LoRA matches full fine-tuning: how much can 400 invoices show?35 min
Interview gym159
- Monday morning: the weekly bookings dashboard shows bookings down 8% week over week. Your director asks for an explanation by noon. How do you approach it?mid10 min
- On a route, load factor rose from 78% to 84% year over year, but revenue fell 3%. Capacity was unchanged. Decompose the change and tell me what you would show the route manager.mid8 min
- Product says the new loyalty app is working: "Members who installed the app last year have 30% higher repeat-booking rates than members who did not." How do you evaluate the claim, and what analysis…senior12 min
- Finance reports monthly ancillary revenue 3% lower than the product analytics dashboard, and both teams say their number is correct. You are asked to resolve it. How?senior12 min
- You must set up automatic alerts on 200 daily KPIs (bookings, revenue, load factor by market and channel).senior12 min
- You join an airline's commercial analytics group. There are 400 dashboards, conflicting KPI definitions, and leadership says "we have lots of data but no answers".staff25 min
- On 30 June an analyst pulls a cohort table of 90-day repeat-booking rate by month of first booking and shows the following hypothetical figures: Jan 17.1%, Feb 16.8%, Mar 17.0%, Apr 15.2%, May 11.1%,…mid10 min
- A funnel dashboard reports search-to-purchase conversion at session level (hypothetical figures): mobile 60,000 sessions and 3,000 purchases (5.0%), desktop 40,000 sessions and 3,000 purchases…senior12 min
- You have events(userid, eventname, eventts) with names 'search', 'select' and 'pay'. Write SQL for the funnel search to select to pay: users reaching each step, step conversion, in the correct order.mid10 min
- A monthly cohort table (hypothetical, data through the end of April) shows customers by first-booking month and the share who booked again in month 1, 2 and 3 after joining.senior12 minin review
- Your customer dimension is a Type 2 slowly changing table customerscd(customerid, tier, validfrom, validto) with validto exclusive and 9999-12-31 for the current row.senior15 minin review
- Marketing adds up the bookings that each channel's own platform report claims: paid search 45,000, social 30,000, email 25,000, affiliate 20,000 (hypothetical monthly figures;staff25 min
- The airline's post-flight survey was sent to 50,000 passengers (hypothetical): 10,000 business-cabin and 40,000 economy. Response rates were 30% and 6%, so 3,000 business and 2,400 economy answered.senior15 min