LEAD DATA SCIENCE (ML Ops)

About the role

Solves complex problems and help stakeholders make data- driven decisions by leveraging quantitative methods, such as machine learning. It often involves synthesizing large volume of information and extracting signals from data in a programmatic way.

What you'll do

• Architect and evolve the ML platform on Databricks (Unity Catalog, MLflow, Feature Store, Model Serving, Workflows) integrated with AWS services
• Define and enforce standards for CI/CD, testing, model promotion, and reproducibility across teams
• Build automated pipelines for training, validation, deployment, and retraining
• Implement monitoring for model performance, data drift, latency, and cost (Lakehouse Monitoring, CloudWatch, Prometheus/Grafana, or similar)
• Own governance: model lineage, access control, audit trails, and compliance with data and regulatory requirements

What you need

• MLOps
• Machine learning
• AWS
• Python
• Databricks
• Spark

Good to have

• Kafka
• CI/CD
• Terraform
• Unity
• IAM
• Jenkins