Senior Data Science Engineer

Key Responsibilities

• ML Pipeline Engineering: Design, build, and maintain scalable ML pipelines leveraging MLFlow, Databricks, and Azure ML.

• Model Development: Develop and optimize ML models, particularly for healthcare predictive analytics.

• Integration: Embed ML models into KPI-driven data processing and analytics workflows.

• Model Lifecycle Management: Implement model monitoring, drift detection, retraining, and continuous improvement strategies.

• Collaboration: Work closely with data engineers, data scientists, and business stakeholders to deploy solutions that deliver measurable impact.