Applied Data Scientist, AI Data Platforms

Groundswell•Washington, DC

1d•$103,307 - $178,090•Hybrid

About The Position

Groundswell is seeking a Data Scientist to help build, configure and operate an AI-assisted platform that helps organizations understand, govern and move complex enterprise data. It combines knowledge graphs, semantic search, machine learning and AI agents with human review. You will work on the ground with a small team to prepare this platform for mission use. Your work spans data preparation, knowledge management, AI evaluation and the analysis that drives platform decisions. You will make sure outputs are accurate, explainable and trusted by mission stakeholders.

Requirements

Bachelor's degree in Computer Science, Data Science, Mathematics, Statistics, Engineering or a related field; advanced degree preferred.
5+ years applying statistics, machine learning, data analysis or related quantitative methods to real-world problems.
Strong Python and SQL skills with common data science libraries (pandas, NumPy, scikit-learn or comparable).
Experience with knowledge graphs, graph data modeling or semantic technologies such as ontologies and taxonomies.
Hands-on experience with language models or AI-assisted workflows, including evaluating output quality.
Must be a U.S. Citizen per contract requirements.
Must be able to obtain and maintain a Public Trust Clearance in accordance with contract requirements.

Nice To Haves

Graph databases and query languages, such as Neo4j and Cypher.
Vector search, embeddings, RAG, and AI agent frameworks such as LangGraph.
Entity resolution, record linkage, and systems for matching, classifying, linking, or ranking records.
Enterprise data migration, ETL, or modernization, particularly for ERP or HR systems.
Time-series analysis, anomaly detection, model monitoring, and drift analysis.
Cloud data and ML services on AWS or another major cloud platform.
Human-in-the-loop AI workflows, experiment tracking, and model evaluation.
Experience working in segmented networks, controlled-change environments, or formal authorization environments.
Active Public Trust Clearance.
Preference given to candidates local to the Washington, DC metro area
A relevant certification in data science, machine learning, cloud or analytics is preferred.

Responsibilities

Translate mission and customer data questions into clear objectives, success measures and evaluation criteria.
Profile, clean and characterize complex multi-source enterprise data. Identify gaps, conflicts, quality issues and relationships that affect downstream use.
Build and maintain the metadata, data dictionaries and reference vocabularies that ground the data platform in each customer environment.
Configure and tune AI-assisted capabilities that match, classify, rank and validate data across systems.
Evaluate AI agents and language-model workflows for accuracy, grounding, consistency and failure modes. Build test sets and track quality over time.
Analyze human review feedback to find systematic errors and improve platform performance.
Support data validation and reconciliation so integrated or migrated data can be trusted.
Partner with data, software and cloud engineers to move validated improvements into production with version control and measurable results.
Create clear visualizations, narratives and briefings for technical and non-technical stakeholders, without overstating certainty.
Handle sensitive data under appropriate access controls, governance and auditability. Maintain reproducible code, experiments and documentation.
Contribute data science expertise to solution planning and proposal efforts as needed.