• Develop end‑to‑end NLP and GenAI solutions, including text classification, summarization, RAG systems, conversational AI, and document intelligence pipelines. • Build, fine‑tune, and evaluate LLM-based models using transformer architectures (BERT, GPT, T5, LLaMA, etc.). • Design and implement custom NLP workflows, embeddings, semantic search, vector databases, and prompt engineering strategies. • Develop scalable advanced ML models leveraging deep learning, traditional ML, and hybrid architectures. • Deploy models and AI apps using modern MLOps practices across cloud environments (Azure preferred). • Collaborate closely with product, engineering, and business teams to translate requirements into AI-driven solutions. • Monitor model performance, conduct error analysis, and continuously optimize pipelines.

• Partner directly with enterprise customers to understand business challenges, identify AI opportunities, and define solution requirements. • Lead customer engagements from discovery and solution design through deployment, adoption, and measurable business impact. • Serve as a trusted technical advisor, helping customers successfully implement and scale AI-driven solutions. • Present technical concepts, solution recommendations, and business outcomes to audiences ranging from practitioners to executive leadership. • Design, build, and deploy scalable machine learning, Generative AI, and agentic AI solutions for real-world supply chain challenges. • Develop production-ready ML pipelines, AI applications, prototypes, and proof-of-concepts. • Evaluate emerging AI technologies and adapt state-of-the-art techniques to customer and product needs. • Ensure solutions balance technical excellence, scalability, and business value. • Collaborate closely with Product, Engineering, Solutions Consulting, Sales, and Customer Success teams to deliver customer outcomes. • Influence product strategy by bringing customer insights, implementation learnings, and industry trends back into the organization. • Mentor team members and contribute to best practices in AI solution development and deployment.

• Serve as a primary facilitator for preparation, execution and follow up for all Planning, Programming, and Budgeting Committee – Guard (PPBC-G) forums (General Officer / Strategic Level forums), to include Resourcing Council of Colonels (RCoC), Resource Integration Steering Committee (RISC). • Coordinates across Army National Guard Bureau staffs to develop topics, products, and presentations for submission to the PPBC-G process. • Support the design, development, and execution of all data analytic efforts led by the GS-13 Data Scientist as directed by the DAG-R Division Chief/Deputy Division Chief in support of the Chief Financial Officer’s analytic agenda. • Provide expertise on ARNG Roles, Missions, Authorities and Army strategic guidance and key planning efforts such as The Army Plan (TAP), Total Army Analysis (TAA), the Army Equipment Modernization Strategy (AEMS), and the Long-Range Investment Requirements Analysis (LIRA). • Develop and enhance websites, applications, and secure access to databases based upon requirements for analytic efforts as approved by the COR.

• Own the end-to-end data science lifecycle for moderately complex models and significant project components — spanning data ingestion, feature engineering, modeling, validation, deployment, monitoring, and retraining. • Apply expertise across several core areas of machine learning and statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference concepts, experimentation design), selecting appropriate methods for complex data science problems. • Write efficient, modular, well-tested code for data processing, feature engineering, and model training/inference, leveraging distributed tooling (e.g., Vertex AI pipelines, Dataflow, BigQuery) where appropriate. • Design and implement robust validation frameworks for complex experiments and models, accounting for potential biases and real-world performance. • Troubleshoot complex model performance issues, data anomalies, and code bugs effectively with little guidance. • Define analytical approaches and scope data science projects for moderately complex or ambiguous business problems. • Partner with product managers and stakeholders to define success metrics and experiment goals, and to translate marketplace problems into data science solutions. • Lead the design and analysis of experiments (e.g., A/B tests, switchback) for your projects, and interpret complex model results and experimental outcomes with a focus on actionable insights and business outcomes. • Proactively identify opportunities within your domain where data science can provide significant value, and initiate exploration. • Follow and help improve established team processes for coding standards, documentation, reproducibility, and experimentation. • Mentor DS I and DS II scientists, providing technical guidance, reviewing code, analyses, and models, and supporting their growth in analytical and modeling skills. • Influence technical decisions within the team regarding modeling choices, validation strategies, and tooling through well-reasoned arguments and expertise. • Drive improvements to team standards, data science best practices, and analytical rigor; take ownership of specific team practices or technical components (e.g., a feature store component, leading experimentation reviews). • Educate stakeholders on the capabilities and limitations of data science models, and clearly explain complex methodologies and findings to both technical and non-technical audiences. • Participate actively in recruiting, providing high-quality, graded interview feedback for candidates up to this level.