Senior Data Scientist - ai & ml - Mlops Enablement

Summary

Embedded Senior Data Scientist on the MLOps Enablement team responsible for shaping ML platform capabilities and tooling used by data scientists across the company. Hybrid role in Seattle requiring in-office presence at corporate headquarters a minimum of four days per week. Focus areas include Vertex AI-based platform features, model evaluation frameworks, feature store design, and DS-native documentation to drive adoption and usability.

Responsibilities

• Run end-to-end POC validation for new platform capabilities such as Feature Store, Endpoints, Model Evaluation, AutoML, and BigQuery ML.
• Embed with DS teams to identify workflow pain points and translate them into reusable platform requirements.
• Design and own a Model Evaluation Framework for batch, online, and streaming use cases.
• Build model-type-aware feature store schemas, endpoint configurations, and evaluation pipelines.
• Benchmark platform choices (e.g., Vertex AI vs SageMaker) for parity, cost, and practitioner ergonomics.
• Author DS-native documentation, onboarding guides, and quickstart notebooks to lower adoption barriers.
• Contribute DS expertise to agentic AI platform initiatives and define evaluation approaches for agent responses.
• Define and implement model card standards that reflect practitioner needs.

Requirements

• Bachelor's, Master's, or PhD in Statistics, Data Science, Computer Science, Engineering, or a related technical field.
• 10+ years of hands-on data science experience delivering production models across multiple model types (classification, ranking, NLP, time-series, recommendation, GenAI).
• Deep expertise in model evaluation, metrics, thresholds, and evaluation pipelines.
• Experience with feature store design, feature engineering, freshness, reuse, and drift considerations.
• Proficiency in Python and delivering production-quality ML code.
• Strong understanding of ML monitoring, including data, prediction, and concept drift detection.
• Experience with experiment tracking, model lifecycle management, CI/CD for ML, containerization, and pipeline orchestration.
• Hands-on experience with GCP and Vertex AI; familiarity with AWS SageMaker for cross-cloud benchmarking preferred.