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
- A marketing director expected a new email campaign to lift bookings. Your test result: +0.8% with a 95% confidence interval of -1.5% to +3.1%. How do you communicate this result?mid8 min
- You spent three weeks analysing why on-time performance fell on regional routes. The COO has five minutes and will read one page.senior15 min
- Two days before a promised launch, you discover that the demand model's validation used a feature that leaks future information.senior12 min
- Tell me about a time an analysis or model you delivered turned out to be wrong. What happened and what did you change? (Behavioural: answer using STAR.)mid8 min
- Tell me about a time you disagreed with a senior stakeholder about a technical or analytical decision and had to influence them without authority. (Behavioural: answer using STAR.)senior10 min
- Tell me about a time you set technical direction across several teams (for example, a shared evaluation standard, a feature platform, or metric definitions) where nobody reported to you.staff15 min
- The VP of customer experience messages you: "Why are customers so unhappy about baggage? I need this for Thursday's leadership meeting." You have survey data, claims data and a customer-service…mid8 min
- The executive team must decide this week whether to launch an AI-assisted pricing feature to all markets.staff20 min
- A colleague's slide shows on-time performance for last quarter and this quarter (96% and 98%) as a bar chart with the vertical axis starting at 94%. The title is "On-time performance doubled".mid8 min
- The route-level weekly demand forecast has a MAPE of about 7% (hypothetical). An operations VP wants to use it for next-day staffing at flight level, where flights are around 150 passengers, and says…senior12 min
- Tell me about a time you decided to stop or significantly de-scope a project that had a lot of sunk cost, and how you brought the people involved with you. (Behavioural: answer using STAR.)staff15 min
- Operations has decided to redefine the company's on-time performance (OTP) KPI: from "share of operated flights arriving within 15 minutes of schedule, measured at wheels-on" to "share of scheduled…staff20 min