Job description

Description: • Support mission-driven federal programs by transforming complex data into actionable insights • Develop machine learning models, perform advanced analytics, and deliver predictive solutions • Directly support initiatives such as predictive maintenance, anomaly detection, and audit analytics • Design, develop, and deploy scalable machine learning models and pipelines • Perform exploratory data analysis (EDA), feature engineering, and statistical modeling • Lead efforts in predictive maintenance, fault detection, and health monitoring of assets • Conduct audit analytics to detect anomalies, identify trends, and support data-driven compliance reviews • Manage ML workflows using tools like MLflow or equivalent MLOps platforms • Collaborate with data engineers, product teams, and client stakeholders to align analytics with mission outcomes • Document methodologies, models, and outcomes in support of governance and auditability Requirements: • 4+ years of experience in applied data science, machine learning, or advanced analytics • Proficiency in Python and PySpark for large-scale data analysis and modeling • Hands-on experience using MLflow or similar platforms for ML lifecycle management • Experience designing and implementing predictive maintenance models (e.g., survival analysis, classification, time series forecasting) • Familiarity with audit analytics, risk indicators, and internal controls • Strong knowledge of statistics, feature engineering, and model evaluation metrics • Experience working with big data platforms (Databricks, Hadoop, Spark) and cloud environments (Azure, AWS, or GCP) • Ability to communicate complex results to technical and non-technical stakeholders Benefits: • Employee Benefits

Requirements