Staff Analytics Engineer, AI & Predictive

What you'll do

• Design, develop, and deploy traditional machine learning models including regression, classification, and time-series forecasting.
• Build and implement agentic AI workflows where autonomous agents orchestrate data access and decision logic.
• Develop Databricks-native applications, including notebook-based apps, interactive dashboards, and parameterized ML workflows.
• Operationalize ML models into production pipelines with integrated monitoring, drift detection, and retraining strategies.
• Build data and ML services/APIs leveraging Python and Databricks Lakehouse capabilities.
• Translate business requirements into end-to-end solutions spanning data ingestion, modeling, and user-facing applications.
• Own production systems by performing troubleshooting, root-cause analysis, and implementing observability logging.
• Influence architectural decisions and technical standards for ML and agentic AI across the organization.

What we're looking for

• 5+ years of hands-on experience in data science, applied machine learning, or ML engineering with ownership of production systems.
• Strong proficiency in Python for ML development, data processing, and application logic.
• Deep experience with traditional ML techniques including regression, classification, clustering, and time series.
• Proven experience building and deploying ML models in production environments.
• Hands-on experience with Databricks, including application development, notebooks, workflows, and ML pipelines.
• Strong understanding of feature engineering, model evaluation, and explainability.
• Experience designing and implementing agentic AI systems or autonomous decision-making workflows (preferred).
• Experience with ML Ops practices, CI/CD, model monitoring, and retraining pipelines (preferred).