Responsibilities: Design and deploy ML models for UXM use cases (anomaly detection, performance forecasting, experience scoring, root cause analysis) Develop feature engineering pipelines from logs, metrics, traces, and endpoint telemetry Integrate AI/ML capabilities into the Data Integration Platform (DIP) architecture and operational dashboards Build and validate MVP models supporting AoA outcomes and UXM operational readiness Implement MLOps pipelines (training, validation, deployment, monitoring) Collaborate with cybersecurity teams to ensure RMF-compliant model deployment Translate model outputs into actionable insights for IT operators and leadership Support dashboard specification, analytics requirements, and operational reporting. Qualifications: Required: Active Secret Clearance 15+ Years of relevant experience (Bachelor's Degree in applicable field may be substituted for 5 years of experience) Minimum 7 years' experience in data science, machine learning, or applied AI (with at least 3 years in operational or enterprise environments) Hands-on experience with: Python-based ML frameworks (TensorFlow, PyTorch, scikit-learn, or equivalent) Time-series analytics and anomaly detection ETL/ELT pipelines and enterprise data platforms SQL and large-scale datasets Experience deploying models into production environments Familiarity with observability, ITOM, or UXM telemetry sources.