Data Science and ML Ops Engineer

Role: Data Science and ML Ops Engineer

Location : hybrid (San Francisco, CA)

Key Responsibilities

Develop predictive models using structured/unstructured data across 10+ business lines, driving fraud reduction, operational efficiency, and customer insights.

Leverage Auto ML tools (e.g., Vertex AI Auto ML, 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., Google Cloud Platform, 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, explain ability)

Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs

Minimum Skills Required:

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.