Description

Key Responsibilities:

Conceptualize research problems, design studies, and lead the development of advanced analytic and ML solutions across supervised, unsupervised, NLP, graph, and (where appropriate) generative-AI techniques.
Translate ambiguous mission questions into clearly defined hypotheses, data requirements, and modeling approaches.
Author and review implementation roadmaps, data exploration reports, model prototype evaluations, and final model analysis reports.
Build, validate, and harden production models — including model cards, bias and fairness assessments, drift monitoring, and reproducibility artifacts.
Lead code reviews, establish coding standards, and mentor data scientists and analysts on the team.
Continuously update and enhance analytic dashboards used to model real-world scenarios and identify potential mission impacts.
Represent the team in technical reviews, working groups, and stakeholder briefings; advise senior project personnel on technical matters.
Stay current on emerging ML, MLOps, and responsible-AI practices and recommend adoption where they advance the mission.

Requirements

Ten (10)+ years of relevant experience in applied research, big data analytics, statistics, applied mathematics, data science, computer science, or operations research.
Seven (7)+ years of direct experience in machine learning.
Master's or Ph.D. in Statistics, Applied Mathematics, Data Science, Computer Science, Operations Research, or a closely related quantitative or technical discipline. (Ph.D. may substitute for up to three years of experience.)
Demonstrated ability to create and validate data mining methods, ML models, and analytical results delivered through reporting and visualization.
Strong communication skills covering analysis techniques, testing, and model validation processes for both technical and non-technical audiences.

Preferred Qualifications:

Experience in financial crime, fraud detection, regulatory analytics, supply-chain, or other high-stakes mission domains.
Hands-on experience with modern NLP / LLMs — including retrieval-augmented generation (RAG), embedding models, fine-tuning, prompt engineering, and evaluation frameworks
Experience with graph analytics for entity resolution, network risk, and link analysis.
Experience with MLOps pipelines, feature stores, model registries, and production monitoring for drift and bias
Publications, patents, or open-source contributions in machine learning.

Tools & Technologies

Languages: Python (pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers, spaCy, NetworkX), R, SQL
ML / MLOps: MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI, Weights & Biases, DVC, Airflow, dbt.
LLMs & GenAI: OpenAI / Anthropic / Bedrock APIs, LangChain, LlamaIndex, vector stores (FAISS, pgvector, Pinecone, OpenSearch).
Big data: Spark / PySpark, Databricks, Snowflake, Dask, Ray
Visualization: Tableau, Power BI, Plotly, Streamlit, Dash.
Cloud (gov): AWS GovCloud, Azure Government.
Collaboration & code: Git/GitHub, Jupyter, VS Code, Docker, Kubernetes.

Clearance & Suitability

U.S. Citizenship required. Candidates must currently possess or be able to favorably pass a five (5) year federal background investigation prior to start. All candidates must clear OneGlobe's pre-screening process, which includes review for felony convictions in the past 36 months, illegal drug use in the past 12 months, relevant misconduct, and a financial background check. Work is primarily UNCLASSIFIED and performed at a federal customer site in the Washington, D.C. metropolitan area, with potential for hybrid arrangements per program policy. Occasional travel may be required.

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