Meta Machine Learning & Data Science interview questions

A 0.95 AUC model can still flood a review queue with garbage. Knowing when ROC AUC flatters a model, and what to report instead, separates FDEs who've shipped from those who've only run notebooks.

A two-minute explanation, then the real test: learning-rate pathologies, why we use mini-batches, and the one practical diagnosis, reading a loss curve, that customer-facing engineers actually need.

Most working data scientists state the definition wrong, and interviewers know exactly which wrong versions to listen for. The correct one-sentence definition, the VP translation, and the three classic traps.

"Run it two weeks and see" fails this question. Strong answers work the power calculation backwards from a minimum detectable effect, and know the duration rules that protect against lying-by-novelty.

Daily peeking can quietly triple your false-positive rate, and almost every customer does it. The math of why, the multiple-comparisons cousin, and how to deliver the bad news without losing the room.

The maturity exam of production ML: a model that's better on paper and worthless in production. Five distinct causes, the funnel diagnostic that separates them, and when the right call is don't ship.

When treatment leaks between units, a clean A/B lies in both directions. The interference taxonomy, when to reach for switchback vs cluster vs geo designs, and the analysis trap that makes naive standard errors useless.

Not leakage, not skew, not drift: the model learned exactly what you asked, and you asked for the wrong thing. The proxy-label mismatch that passes every test, the audit that catches it, and why this is the failure no metric can see.

Bandits sound strictly better and usually are not. The regret-vs-inference tradeoff, the three conditions that actually favor a bandit, and the production failure that makes adaptive allocation a debugging nightmare.

Backprop is just the chain rule run efficiently in reverse, but the question that separates levels is why a hundred-layer network trains when a naive one wouldn't. The credit-assignment intuition plus the three tricks that keep gradients alive.

The answer is not just 'transformers are better.' It is two specific wins: every position can attend to every other in one step (long-range dependencies) and the whole sequence trains in parallel instead of one token at a time. Plus the cost that buys, and where RNNs still make sense.

Convolution and parameter sharing are why a CNN needs a tiny fraction of the weights a dense net would, and why it generalizes across position. The conceptual answer, the feature-hierarchy picture, and the honest take on when a fine-tuned CNN still beats reaching for a giant vision-language model.

Accuracy is meaningless for ranking, the whole point is the order. The real question behind the question is whether your relevance is graded or binary and whether one good result is enough, because that picks the metric. Then the trap: your offline NDCG can climb while online engagement falls.

The thing interviewers actually screen for isn't the Bellman equation, it's whether you know when NOT to use RL. Most problems that look like RL are better solved by supervised learning or a bandit, and reaching for full RL when you have labels is a classic over-engineering tell.

Almost no labels means you cannot train a classifier and you cannot honestly report recall. The senior answer is to score by typicality, threshold by the customer's review budget, and build the labeling flywheel that turns this into a supervised problem within two quarters.

Every FDE loop opens with this, but the definitions are table stakes. What gets scored is whether you can map each metric to a dollar cost the customer recognizes, here's the framing that does it.

This is a discovery question wearing an ML costume. The strongest candidates run a four-step translation from business pain to loss function, and put a number on each error before choosing anything.

The textbook answer gets a polite nod. The version that names which side of the tradeoff a 6-week customer pilot usually dies on gets the hire signal, here's that version.

This exact gap kills more customer pilots than any modeling choice. There's a ranked list of culprits, and one of them hides inside innocent-looking feature pipelines at almost every enterprise.

Every RAG deployment runs on this math, and most candidates can't say why cosine beats Euclidean, or when the two are secretly identical. The 90-second version with a worked example.

Time-series is where standard ML habits, random splits, fancy models first, single-number forecasts, fail loudest in front of customers. The baseline discipline and backtesting setup that keep you credible.