Lead AI/ML Innovation in Mission Data Science DeNOVO Solutions is seeking a Senior Data Scientist Engineer to lead the design, development, and deployment of advanced AI/ML solutions supporting complex mission and operational environments. This role is ideal for an experienced data science professional who thrives at the intersection of research, engineering, and mission execution developing high impact, explainable models that transform raw data into actionable intelligence. You’ll guide the evolution of production ML systems, mentor mid level engineers, and work directly with analysts to ensure model performance meets the highest standards of accuracy, transparency, and operational readiness. Why You’ll Love This Role: You’ll Shape AI for the Mission: Design and deploy models that extract real world value from signal and sensor data. You’ll Drive Scalable ML Systems: Lead the integration of automated retraining and deployment pipelines within secure cloud environments. You’ll Mentor and Lead: Share your expertise with junior data scientists and engineers, fostering a culture of innovation and technical excellence. A Day in the Life: AI/ML Solution Development: Lead the creation and optimization of advanced ensemble, neural network, and unsupervised learning models tailored to mission data. Automation & Scalability: Design automated pipelines for data ingestion, curation, and retraining to ensure model adaptability and long term performance. Validation & Explainability: Coordinate with analysts and mission experts to validate models, track accuracy metrics, and enforce compliance with explainability and transparency standards. Model Deployment: Integrate and operationalize ML models into production systems, collaborating with DevSecOps to align with CI/CD pipelines and mission constraints. Monitoring & Optimization: Implement tools for continuous monitoring, A/B testing, and drift detection to maintain model reliability. Team Leadership: Mentor junior team members, review code and models, and promote best practices in data science methodology, governance, and reproducibility. Tools, Technologies, Skills, & Knowledge: Machine Learning & AI: Ensemble models, CNNs, RNNs, Transformers, unsupervised and semi supervised learning Frameworks & Libraries: TensorFlow, PyTorch, Scikit learn, XGBoost Data Infrastructure: Feature stores, distributed computing frameworks (Spark, Ray, Dask) Deployment & Monitoring: MLflow, DVC, Docker, model monitoring, A/B testing, data drift detection Analytical Expertise: Causal inference, signal data processing, experiment design, model interpretability (SHAP, LIME) Collaboration Tools: Jupyter Notebooks, Git, Confluence, Agile/DevSecOps environments What You Need: Experience: Ten (10) to fifteen (15) years of experience designing, training, and deploying ML models within the DoD or Intelligence Community, including experience leading technical teams or enterprise AI initiatives. Education: Bachelor’s degree or higher in a quantitative discipline (e.g., statistics, mathematics, operations research, engineering, or computer science). A Master’s degree may substitute for two years of experience; a PhD may substitute for four years. An additional two years of related experience may substitute for a Bachelor’s degree. Certifications: None required.