Senior Data Scientist - AI & ML - MLOps Enablement

brobstongroup.com - Jobboard; Seattle (WA); Hybrid; USD 120,000 - 160,000; Full time

Job summary

brobstongroup.com - Jobboard seeks an experienced Embedded Senior Data Scientist for the MLOps Enablement team in Seattle. This hybrid role focuses on shaping ML platform capabilities and requires in-office presence at least four days a week.

The successful candidate will run end-to-end POC validation and design model evaluation frameworks, alongside a strong background in data science and machine learning.

Qualifications

• Bachelor's, Master's, or PhD in Statistics, Data Science, Computer Science, Engineering, or related technical field.
• 10+ years of hands-on data science experience delivering production models across multiple model types.
• Deep expertise in model evaluation, metrics, thresholds, and evaluation pipelines.

Responsibilities

• Run end-to-end POC validation for new platform capabilities.
• Embed with DS teams to identify workflow pain points.
• Design and own a Model Evaluation Framework.

Skills

Data science

Machine Learning

Python

Model evaluation

Feature engineering

GCP

Vertex AI

AWS SageMaker

Education

Bachelor's, Master's, or PhD in Statistics, Data Science, Computer Science, Engineering, or related field

Tools

CI/CD for ML

Containerization

Pipeline orchestration

Job description

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.