Senior Decision Intelligence Engineer (NBA)

Become a part of our caring community   The Senior Decision Intelligence Engineer is a hands-on individual contributor responsible for building, deploying, and operating ML and decisioning pipelines for the NBA Decision Intelligence Platform. This role focuses on production pipeline development, MLOps, feature engineering, scoring workflows, monitoring, and optimization-aware decisioning. You will help ensure the platform selects the right action for the right member while respecting clinical eligibility, suppression rules, channel constraints, program goals, and operational capacity. You will work closely with ML engineers, data engineers, platform engineers, product owners, and decision engine teams to deliver reliable, scalable, and auditable production systems. ML Pipeline and MLOps Development Build and maintain production pipelines for feature generation, model training, evaluation, scoring, deployment, and monitoring. Develop reusable pipeline components using Python, PySpark, Databricks, Delta Lake, and MLflow. Support CI/CD, automated validation, model versioning, artifact management, rollback, and release workflows. Monitor model performance, data quality, drift, scoring outcomes, and operational health. Troubleshoot production issues across feature pipelines, scoring jobs, model artifacts, and downstream integrations. Feature Engineering and Scoring Build and maintain member feature pipelines using clinical, behavioral, engagement, operational, web clickstream, and socioeconomic data. Ensure features are reproducible, auditable, governed, and performant within the Databricks Lakehouse. Operate batch and near-real-time scoring workflows that support personalized outreach across email, SMS, direct mail, digital, and care team channels. Integrate model outputs into decisioning platforms, campaign systems, and operational workflows. Decisioning and Optimization Contribute to optimization logic for next-best-action selection, constrained ranking, member prioritization, and resource allocation. Translate business and clinical rules into structured constraints, scoring adjustments, objective functions, and prioritization logic. Help balance member relevance, program objectives, outreach limits, channel availability, eligibility rules, suppression periods, and operational capacity. Monitor decisioning behavior for scoring anomalies, constraint violations, data issues, and SLA risks. Experimentation, Safety, and Governance Support A/B testing, holdout testing, and measurement workflows for evaluating model and decisioning effectiveness. Build automated evaluation gates to prevent underperforming models or scoring workflows from being promoted. Validate output quality, action distributions, rule alignment, and downstream decision behavior before production release. Document pipeline behavior, assumptions, known limitations, and operational runbooks. Support compliance, auditability, explainability, and privacy expectations in a regulated healthcare environment. Collaboration and Engineering Practices Partner with ML engineering, data engineering, platform, product, rules engine, and decision engine teams. Participate in design reviews, code reviews, operational readiness reviews, and production support. Communicate implementation tradeoffs, production risks, optimization assumptions, and delivery status clearly. Use AI-assisted engineering tools such as GitHub Copilot, Claude, or similar platforms to improve development speed and quality. Use your skills to make an impact   Requirements 5+ years of experience in machine learning engineering, MLOps, data engineering, platform engineering, optimization engineering, or related software engineering roles. Strong hands-on experience with Python, PySpark, SQL, and distributed data processing. Experience building and operating production ML pipelines using Databricks, MLflow, Airflow, or equivalent platforms. Experience with CI/CD, testing, observability, deployment