> Markdown version of [/jobs/ext/3969597-data-scientist](https://www.wearedevelopers.com/jobs/ext/3969597-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Scientist

- **Company:** Black Canyon Consulting LLC
- **Location:** Bethesda, MD, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Artificial Intelligence, Data Analysis, Data Files, Python (Programming Language), Machine Learning, Natural Language Processing, Standard Sql, Unstructured Data, Enterprise Search, Evaluation Pipelines, Retrieval-Augmented Generation, Large Language Models, Grafana, Agentic-AI, Kubernetes, Information Technology, Intelligent Document Processing, OpenSearch
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/data-scientist-bethesda-md--0ea588bd-8b7d-4805-a8ac-dff6eafe98dd

## About the Role

Data Science, Natural Language Processing (NLP), Data Science, Product Development, Machine Learning, Data Quality, Analytical Development, Data Analysis, Artificial Intelligence (AI) Natural Language, Data Sets, * Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, or a related quantitative discipline., * 5+ years of relevant data science, applied machine learning, or quantitative analytics experience.

 * Strong Python and SQL skills.
 * Experience with statistical analysis, machine learning, NLP, and unstructured text data.
 * Experience designing labeled datasets, benchmark datasets, and train/test evaluation frameworks.
 * Experience evaluating LLM, RAG, or agentic AI solutions, including retrieval quality, output accuracy, and failure modes.
 * Strong understanding of model-evaluation techniques, including precision, recall, F1, ranking metrics, confusion matrices, and related measures.
 * Experience with embeddings, semantic retrieval, or vector-based search.
 * Experience developing reproducible analytical and evaluation workflows.
 * Ability to distinguish data-quality issues from model-performance issues.
 * Strong ability to communicate analytical results, limitations, and uncertainty.

Bonus Skills

 * Experience with agent evaluation frameworks or LLM observability tools.
 * Experience with agentic frameworks such as LangChain, LangGraph, or similar orchestration frameworks.
 * Experience with OpenSearch, vector databases, or enterprise search platforms.
 * Experience with document AI or OCR pipelines.
 * Experience with geospatial analysis.
 * Experience developing entity-resolution, record-linkage, or data-quality models.
 * Experience working in the U.S. Federal Government space.
 * Experience with permitting, regulatory, environmental, energy, or natural-resource datasets.

## Description

* Develop analytical and machine-learning approaches supporting permitting and natural-resource use cases.
 * Build and maintain labeled datasets, evaluation corpora, and benchmark datasets for AI-enabled services.
 * Define quantitative evaluation methodologies for classification, extraction, retrieval, summarization, entity matching, and other AI/NLP capabilities.
 * Evaluate LLM and agentic workflows, including output quality, retrieval performance, task completion, failure modes, and reliability.
 * Analyze public-comment datasets and develop reusable features and evaluation datasets for comment-processing services.
 * Apply NLP and machine-learning techniques to structured and unstructured data.
 * Evaluate embedding models, semantic retrieval strategies, document-ranking techniques, and AI-generated outputs.
 * Develop statistical approaches for detecting anomalies, reconciliation issues, missing data, and inconsistent records.
 * Partner with Content Pipeline Engineers to measure OCR, extraction, and document-retrieval performance.
 * Partner with Data Engineers to ensure analytical datasets and evaluation pipelines are reproducible and production-ready.
 * Support entity matching and harmonization across data originating in multiple source systems.
 * Design experiments and validation approaches with clearly defined success metrics.
 * Contribute to certified analytical data products, metrics, and governed datasets.
 * Develop reproducible Python analyses, models, evaluation pipelines, and technical documentation.
 * Communicate model performance, limitations, uncertainty, and failure modes to technical and non-technical stakeholders.