Qualifications

• Proficient in Python, with SQL skills; familiarity with Java or Scala is a plus.
• Experience with MLflow or similar tools for MLOps, including model monitoring and pipelines.
• Knowledgeable in data engineering concepts, particularly Spark and DataFrames.
• Familiar with DevOps practices, including CI/CD, Docker, and optional experience with Kubernetes or Terraform.
• Experience working with Azure Cloud services and relevant logging/monitoring tools.
• Demonstrated experience in MLOps, emphasizing model versioning and pipeline automation.

Responsibilities

• Lead the development and implementation of production ML pipelines, from training to real-time scoring.
• Transform Data Science prototypes into reliable and maintainable services with optimal observability.
• Design and maintain feature engineering workflows and reusable ML components.
• Oversee model deployment patterns, ensuring efficiency in performance and cost management.
• Implement CI/CD processes for ML using Azure DevOps or GitHub Actions integrated with Databricks.
• Utilize MLflow for model tracking and lifecycle management, establishing best monitoring practices.
• Collaborate with Data Engineering to ensure high data quality and integrity for model inputs.

Benefits

• Discretionary annual incentive program.
• Comprehensive medical coverage including dental and vision, as well as disability planning.
• Family support options including maternity and parental leave.
• Legal and financial assistance including 401K plan and student loan refinancing.
• Convenience benefits such as commuter options and certification reimbursements.
• Generous time off policy including vacations, sick leave, and holidays.