Designing a self-serve experimentation platform
WHAT IT TESTS: building safe experimentation as a platform, not a service. OUTLINE: SDK with sane defaults, automated pre-launch validation, sample-ratio and guardrail-metric checks.
Sensitivity analysis on a growth model
WHAT IT TESTS: finding leverage in a coupled model. OUTLINE: perturb each input by a normalized amount, measure the change in the long-term output, and use elasticities or global methods to rank drivers.
Build a SaaS churn prediction model
WHAT IT TESTS: end-to-end supervised modeling with a clear label. OUTLINE: define churn and the prediction window, engineer usage-trend and tenure features, try logistic regression then gradient-boosted trees, and evaluate on class-imbalanced metrics.
Peeking in A/B tests and how to mitigate it
WHAT IT TESTS: understanding inflated false positives from repeated looks. OUTLINE: peeking is checking significance repeatedly and stopping at the first significant result, which inflates false positives; mitigate with fixed sample sizes or sequential…
Cold-start to personalized feed transition
WHAT IT TESTS: graceful handling of sparse early signal. OUTLINE: start with popularity or onboarding-declared interests, collect implicit signals like dwell and clicks, then blend toward personalized as confidence grows.
Dynamic personalized onboarding architecture
WHAT IT TESTS: closing the loop from data to decision to learning. OUTLINE: a segmentation pipeline, a serving layer choosing task order per segment, an experimentation engine, and a feedback loop measuring activation.
Cross-platform stateful onboarding sync
WHAT IT TESTS: server-authoritative state with multi-client sync. OUTLINE: store onboarding state server-side keyed to the user, expose idempotent step-completion APIs, and push updates to other clients.
Implement a welcome-message A/B test
WHAT IT TESTS: end-to-end client experiment basics. OUTLINE: deterministic hash of a stable ID for sticky assignment, conditional rendering of the personalized variant, and exposure plus click tracking keyed to the same ID.
Detect fraudulent app installs
WHAT IT TESTS: signal design for install fraud. OUTLINE: click-to-install timing distributions, device and IP fingerprints, post-install engagement, and attribution anomalies.
Explain deferred deep linking flow
WHAT IT TESTS: bridging context across the install gap. OUTLINE: capture link payload server-side at click, route to the store, then match the new install to the click on first launch to route the user.
Design a unique referral code system
WHAT IT TESTS: uniqueness under concurrency and code-space sizing. OUTLINE: a unique DB constraint as the source of truth, generation via random retry or an encoded counter, and collision handling.
Capture UTM params and attribute on signup
WHAT IT TESTS: attribution plumbing across an anonymous-to-known transition. OUTLINE: parse UTMs on landing, persist them in a cookie tied to an anonymous ID, then stamp them onto the account at signup.
Communicate experiment results and check guardrails
WHAT IT TESTS: translating statistics and protecting against harm. OUTLINE: explain the lift and confidence in plain business terms, report a confidence interval not just a point, and verify guardrails before shipping.
Components of a testable A/B hypothesis
WHAT IT TESTS: experimental rigor before launch. OUTLINE: a specific change, a predicted directional effect on one primary metric, a rationale, and a measurable success threshold.
Design a streak-saver mechanism
WHAT IT TESTS: modeling stateful engagement with behavioral nuance. OUTLINE: timezone-aware streak state, capped saver inventory with replenish rules, and A/B testing forgiveness against retention plus guardrails.
Migrate a breaking analytics schema change
WHAT IT TESTS: safe rollout of a breaking data contract. OUTLINE: dual-write both fields during overlap, backfill history, migrate consumers, then deprecate the old field.
Design an analytics event schema
WHAT IT TESTS: discipline in analytics taxonomy. OUTLINE: consistent object-action naming, snake_case, typed properties with units, and shared context like user, session, timestamp.
Client-side vs server-side event tracking
WHAT IT TESTS: judgment on tracking placement. OUTLINE: client captures UI intent but loses data to ad blockers and tampering; server is trustworthy for transactions but blind to UI interactions.
Experiment design under network effects
WHAT IT TESTS: awareness that SUTVA breaks under interference. OUTLINE: cluster-level randomization, graph or geo clustering to contain spillover, and analysis at the cluster unit.
Design a centralized experimentation service
WHAT IT TESTS: platform thinking for shared experimentation. OUTLINE: a config/assignment API, deterministic SDK-side bucketing, and a separate exposure-logging pipeline.