Amazon·Data Scientist·Technical Phone Screen
May 2026
Brutal technical screen for a Data Scientist role at Amazon. Eight questions covering everything from gradient descent derivations to LoRA fine-tuning on a single GPU. Whoever wrote this question set really wanted to see if you could go deep on ML fundamentals, RL, transformers, and LLM infra all in one sitting.
- Derive the update rules for full-batch gradient descent and SGD for a mean loss over n samples. Compare their convergence behavior, gradient variance, and wall-clock efficiency. When does SGD actually win?
- Define batch size. Given 50,000 samples, 5 epochs, and batch size 200, how many update steps per epoch and in total? If you increase batch size to 2,000, what changes and how should you adjust the learning rate? When does the linear scaling rule break down?
- For each of the following, say whether it's supervised or unsupervised and give one real use-case: logistic regression, SVM, k-NN, k-means, PCA, t-SNE, Isolation Forest.
- How does reinforcement learning relate to supervised and unsupervised learning? Write out the REINFORCE policy gradient estimator and show how a baseline keeps the estimator unbiased while reducing variance. Then compute the gradient for a 3-step trajectory with returns [3, 1, -1] and a constant baseline equal to the mean of those returns.
- How do neural networks fit into RL? Specifically for DQN, why do target networks and experience replay stabilize training, and what goes wrong if you remove them?
- Compare Transformers and RNNs on parallelism, long-range dependencies, and computational complexity. For sequence length 1024 and model dimension 512, what are the asymptotic time and memory costs of self-attention? Name two approaches to reduce the quadratic scaling.
- What are embeddings and what is polysemy? Propose a method to distinguish the word 'King' in a chess context versus a monarchy context. How would you evaluate it both intrinsically and extrinsically?
- You have one 24GB GPU and a 7B parameter model. Design a fine-tuning plan that fits within those constraints. Walk through your choices for quantization, adapter method, optimizer, and learning rate schedule. Estimate how many trainable parameters you'd actually have with LoRA, assuming hidden size ~4096 and ~32 layers.
“This is the kind of question that sounds easy until you're actually writing out the math live.” The rest of the author's notes on Data Scientist interview at Amazon, Technical Phone Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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