Data Scientist

The position will focus on building data ingestion and data transformation infrastructure to interface with data repository application program interfaces (API) and build AI/ML models to enterprise standards for data sharing with joint enterprise systems., The Data Scientist will develop predictive models for operational logistics, including: o Demand Forecasting Models: Utilize time-series analysis (e.g., ARIMA, Exponential Smoothing) and machine learning models (e.g., Random Forest, Gradient Boosting, XGBoost) for demand prediction and resource optimization. o Inventory Optimization Models: Develop algorithms to improve the efficiency of supply chain operations, utilizing linear programming, mixedinteger optimization, and supply chain simulation tools. o Anomaly Detection: Implement unsupervised learning techniques such as k-means clustering, DBSCAN, and autoencoders for the detection of anomalies in supply chain operations and logistics performance. o Advanced Statistical Modeling: Apply advanced statistical techniques to assess sustainment risks and optimize logistic workflows. The Data Scientist will handle the breadth of tasks related to model development, training, and continuous optimization of predictive models. The diversity in machine learning algorithms, coupled with the need to manage and analyze large volumes of data from multiple sources, requires a larger team. These professional will also need to continuously monitor model performance, recalibrate models with updated data, and deploy models in production environments.

Bachelor's degree and a minimum of 7 years related experience, US Citizen - TS/SCI clearance