Senior Data Scientist

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.

• 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.
• Masters 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 OneGlobes 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.