Data Scientist SME

The Data Scientist SME will serve as a senior technical subject-matter expert responsible for designing, evaluating, and implementing AI, Generative AI, machine learning, and agentic AI solutions supporting regulatory workflows, knowledge management, document processing, data quality, and decision support for our client.., * Identify and evaluate high-value opportunities to apply AI, Generative AI, machine learning, and agentic AI to existing USFWS regulatory and business processes.

• Design AI-first and agentic workflows that improve process efficiency while maintaining appropriate levels of human oversight and control.
• Develop multi-step agentic workflows for research, document analysis, knowledge retrieval, classification, validation, and decision support.
• Design and implement NLP and LLM-based approaches for analyzing unstructured text, regulatory documents, scientific information, and historical records.
• Develop AI-assisted knowledge-management capabilities for retrieving, organizing, summarizing, and analyzing scientific and regulatory information.
• Develop machine-learning and AI-assisted approaches for:
• Data classification and categorization.
• Anomaly and outlier detection.
• Automated data-quality validation.
• Record validation and reconciliation.
• Document and field extraction.
• Controlled-vocabulary and taxonomy mapping.
• Design and implement embedding-based similarity and semantic matching approaches.
• Develop and evaluate Retrieval-Augmented Generation (RAG) solutions for regulatory and scientific knowledge.
• Define confidence thresholds and establish human-in-the-loop review workflows for low-confidence or high-risk AI outputs.
• Establish measurable model-performance criteria, including precision, recall, accuracy, F1 score, and other appropriate evaluation metrics.
• Develop labeled evaluation datasets and testing methodologies to assess AI and machine-learning performance.
• Evaluate model outputs for accuracy, consistency, relevance, explainability, and potential failure modes.
• Design approaches for automated summarization, information retrieval, document analysis, and scientific/regulatory knowledge discovery.
• Work closely with data engineers to define and prepare AI-ready datasets for model development, testing, and deployment.
• Ensure AI solutions maintain appropriate traceability, explainability, reproducibility, and auditability.
• Establish responsible-AI practices addressing model limitations, human oversight, data quality, bias, and appropriate use of automated decisions.
• Collaborate with business analysts, data engineers, software developers, architects, and subject-matter experts to translate business problems into practical AI solutions.
• Develop proof-of-concepts and reference implementations using Python and/or R.
• Document AI methodologies, model assumptions, evaluation results, prompts, workflows, data requirements, and implementation approaches.
• Support the transition of successful AI prototypes into production-ready solutions.
• Monitor and evaluate AI solutions over time to identify model degradation, data-quality issues, and opportunities for improvement.

The role will focus on identifying high-value opportunities for AI-driven modernization, developing practical AI and machine-learning approaches, and establishing measurable evaluation and human-in-the-loop processes. The ideal candidate will have strong hands-on experience with Python, NLP, LLMs, embeddings, RAG, classification, information extraction, and agentic workflows, along with a strong understanding of responsible AI, model evaluation, explainability, and data governance., * 8-12+ years of experience in data science, machine learning, artificial intelligence, or a related field.

• Strong hands-on programming experience with Python.
• Strong experience developing and evaluating machine-learning and AI solutions.
• Hands-on experience with NLP, large language models (LLMs), embeddings, classification, and information extraction.
• Experience designing and implementing Generative AI solutions for real-world business or technical use cases.
• Experience with Retrieval-Augmented Generation (RAG) and semantic search/retrieval approaches.
• Experience designing agentic AI or multi-step AI workflows with appropriate human oversight.
• Strong understanding of model evaluation methodologies, including precision, recall, F1 score, accuracy, and other appropriate performance measures.
• Experience developing labeled datasets, evaluation frameworks, and model-quality benchmarks.
• Experience applying AI/ML to unstructured and semi-structured data.
• Strong understanding of data quality, validation, classification, anomaly detection, and information extraction.
• Experience designing human-in-the-loop processes and confidence-based review workflows.
• Strong understanding of responsible AI principles, including explainability, traceability, reproducibility, and human oversight.
• Ability to collaborate effectively with data engineers, software developers, architects, business analysts, and subject-matter experts.
• Strong analytical, problem-solving, communication, and technical documentation skills.

Preferred Qualifications

• 3+ years of hands-on experience with modern Generative AI, LLM, and/or agentic AI technologies.
• Experience with OCR and document intelligence solutions.
• Experience applying AI/ML to scientific, regulatory, environmental, legal, or government datasets.
• Experience with AWS AI, machine-learning, and data services.
• Experience developing AI solutions for federal government or regulated environments.
• Experience working with sensitive, CUI, PII, or otherwise controlled data.
• Knowledge of AI governance, model risk management, and responsible-AI frameworks.
• Experience designing AI solutions requiring auditability, explainability, and human approval.
• Experience with scientific or regulatory knowledge-management systems.
• Experience with controlled vocabularies, taxonomy mapping, entity resolution, or master-data management.
• Experience developing production-oriented AI prototypes and reference implementations.
• Experience supporting digital transformation or modernization initiatives involving AI and automation. *

We know tech, but we love people. NuAxis is home to thinkers and feelers; engineers and artists. We work hard and support each other along the way. Teamwork is more than just a buzzword for us, it’s a state of mind.