Senior Data Scientist

Role: Senior Data Scientist / ML Engineer with AWS

Location: Remote

Summary: Works on data to build, train and deploy next-generation LLM-based recommender systems. The role covers the full path from raw customer data to a deployed model.
Responsibilities:

·         Data to training sets: turn unstructured and structured traveler and catalog data into narrative text documents and synthetic data, at a scale of tens of millions of documents.

·         Feature and context engineering: decide what information goes into each document and how to express it, such as market-relative price, tier and trip context.

·         Large-scale distributed training on Amazon SageMaker AI, including HyperPod.

·         Fine-tuning: LoRA/QLoRA adapters, supervised training, and reinforcement learning for ranking and reasoning.

·         Deep learning modeling: model architecture, ranking heads, co-training and multi-task setups.

·         Inference and deployment: low-latency, cost-efficient serving, including quantization, distillation and profiling.

·         Evaluation and error analysis: build evaluation sets, run ablations and catch data leakage. Write up findings clearly for the customer and the AWS science team.

·         Data privacy: handle customer data safely, including removing per

Required skills

·         Spark (PySpark) and the Hadoop ecosystem (Hive, HDFS) on corpora of tens of millions of documents.

·         Amazon SageMaker AI: processing, training, distributed training (HyperPod), deployment.

·         LLM fine-tuning (LoRA/QLoRA) and reinforcement learning (e.g. GRPO/PPO-style methods).

·         Strong deep learning fundamentals in PyTorch: architectures, training dynamics, co-training.

·         Large-scale batch LLM inference and production inference (vLLM, Amazon Bedrock batch).

·         Data privacy and security on AWS: personal-data removal, IAM, KMS, Lake Formation.

·         Clear written communication with customer and science stakeholders.

Preferred skills

·         AWS Trainium / Inferentia (Neuron SDK).

·         Recommender systems: ranking metrics (NDCG, MRR), sequential and generative recommenders.

·         Embedding quantization or semantic IDs.

·         Experience in a customer-facing or embedded role.