Job Details

Overview

Position: Data Science & ML Ops Engineer. Location: SF Bay Area ONLY (San Leandro preferred) (5 days onsite). Duration: Contract (W2 Candidate Only).

Responsibilities

• Develop predictive models using structured and unstructured data across multiple business lines to drive fraud reduction, operational efficiency, and customer insights.
• Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no-code model development, documentation automation, and rapid deployment.
• Develop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI.
• Automate model training, testing, deployment, and monitoring in cloud environments (e.g., GCP, AWS, Azure).
• Implement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining.
• Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM, documentation, explainability).
• Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs.
• Demonstrate strong proficiency in Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
• Experience with cloud platforms and containerization (Docker, Kubernetes).
• Familiarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks.
• Solid understanding of software engineering principles and DevOps practices.
• Ability to communicate complex technical concepts to non-technical stakeholders.

Qualifications

• Experience understanding of Google/Azure and Spark/Python with ML Ops.
• Record of roles spanning data science and ML engineering; strong ML engineering with data science knowledge considered.
• Proficiency in Python, SQL, and ML libraries (scikit-learn, XGBoost, TensorFlow, PyTorch).
• Experience with cloud platforms and containerization (Docker, Kubernetes).
• Familiarity with data engineering tools (Airflow, Spark) and ML Ops frameworks.
• Strong communication skills and ability to explain technical concepts to stakeholders.