Data Scientist

K&K Global Talent Solutions Inc. is an international recruiting agency that has been providing technical resources in the Canada and the USA region since 1993.

This position is with one of our clients in USA, who is actively hiring candidates to expand their teams.

Role:- Data Scientist   

Location:- San Jose, CA (Remote)   

Fulltime

Responsibilities:
1. 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.
2. 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.
3. Large-scale distributed training on Amazon SageMaker AI, including HyperPod.
4. Fine-tuning: LoRA/QLoRA adapters, supervised training, and reinforcement learning
for ranking and reasoning.
5. Deep learning modeling: model architecture, ranking heads, co-training and
multi-task setups.
6. Inference and deployment: low-latency, cost-efficient serving, including quantization,
distillation and profiling.
7. 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.
8. 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.