Staff Data Scientist

Design, develop, and deploy traditional machine learning models, including regression, classification, clustering, time series forecasting, and anomaly detection.
• Perform feature engineering, model selection, training, validation, and performance tuning on large scale enterprise datasets.
• Apply sound statistical and ML best practices to ensure model robustness, explainability, and business relevance.
Agentic AI & Intelligent Automation
• Design and implement agentic AI workflows, where autonomous or semi autonomous agents orchestrate data access, ML inference, decision logic, and actions.
• Build multi step agent pipelines that combine rules, ML models, and reasoning components to solve complex business problems.
• Integrate agentic systems with enterprise data, ML models, and applications to enable intelligent automation and decision support.
Databricks Application Development
• Design and develop Databricks native applications, including notebook based apps, interactive dashboards, and parameterized data/ML workflows.
• Build data and ML services/APIs leveraging Databricks, Python, and Lakehouse capabilities.
• Partner with analytics, BI, and application teams to embed ML insights, predictions, and agent outputs directly into Databricks apps and business workflows.
• Ensure Databricks apps meet performance, security, governance, and usability standards.
ML Engineering & Productionization
• Operationalize ML models and agentic workflows into production pipelines, ensuring scalability, reliability, and monitoring.
• Collaborate with data engineering teams to leverage curated Lakehouse data, feature stores, and governed datasets.
• Implement model monitoring, drift detection, and retraining strategies to maintain long term model effectiveness.
Full Stack Data Enablement
• Develop end to end solutions that span data ingestion, modeling, ML inference, agent execution, and user facing applications.
• Translate business and analytical requirements into scalable, maintainable ML powered data products.
• Enable downstream consumption through Databricks apps, dashboards, APIs, and integrated enterprise applications.
Production Support & Operational Excellence
• Own production ML models, agentic systems, and Databricks applications, including monitoring, troubleshooting, and root cause analysis.
• Implement logging, alerting, and observability for models, agents, and applications.
• Drive continuous improvements in model accuracy, system reliability, and user experience.
Technical Leadership & Influence
• Serve as a technical authority in traditional ML, agentic AI, and Databricks application patterns.
• Influence architectural decisions, best practices, and technical standards across teams.
• Mentor peers and raise the bar on ML rigor, engineering quality, and production readiness.

• 10+ years of hands on experience in data science, applied machine learning, or ML engineering, with ownership of production systems.
• 5+ years of experience in building RAG based GenAI agentic applications and workflows
• Strong proficiency in Python for ML development, data processing, and application logic.
• Deep experience with traditional ML techniques (e.g., regression, classification, clustering, time series).
• Proven experience building and deploying ML models in production environments.
• Hands on experience with Databricks, including Databricks application development (notebooks, workflows, dashboards, ML pipelines).
• Strong understanding of feature engineering, model evaluation, and explainability.
• Experience collaborating with data engineering, BI, and application teams.

Bachelor's or Master's degree in Computer Science, Data Science, Information Technology, or a related field.
• Familiarity with Lakehouse architectures, feature stores, and ML lifecycle management.
• Experience with MLOps practices, CI/CD, model monitoring, and retraining pipelines.
• Exposure to cloud platforms (e.g., AWS) and scalable ML infrastructure.
• Experience embedding ML and agent outputs into enterprise applications or analytics platforms.
• Knowledge of data governance, access controls, and secure ML deployment.