Senior Data Scientist (ML/NLP)

TL;DR

Senior Data Scientist (ML/NLP/GenAI): Conceptualize research problems, design studies, and lead development of advanced analytic and ML solutions across supervised, unsupervised, NLP, graph, and generative-AI techniques. Focus on translating ambiguous mission questions into hypotheses, building and validating production models with bias assessments and drift monitoring, and mentoring data scientists.

Location: Washington, D.C. metropolitan area at federal customer site with potential for hybrid arrangements per program policy. U.S. Citizenship required. Must possess or pass a five-year federal background investigation. Occasional travel may be required.

Company

hirify.global delivers advanced analytics and ML solutions for federal customers in high-stakes domains.

What you will do

• Conceptualize research problems, design studies, and lead ML solutions using supervised, unsupervised, NLP, graph, and generative-AI techniques.
• Translate ambiguous questions into hypotheses, data requirements, and modeling approaches.
• Author roadmaps, data reports, model evaluations, and final analysis reports.
• Build, validate, and productionize models including model cards, bias assessments, and drift monitoring.
• Lead code reviews, establish standards, mentor team members, and update analytic dashboards.
• Represent team in reviews, brief stakeholders, and advise on technical matters while staying current on ML and MLOps practices.

Requirements

• 10+ years in applied research, big data analytics, statistics, data science, or related fields; 7+ years in machine learning.
• Master's or Ph.D. in Statistics, Applied Math, Data Science, CS, Operations Research, or similar (Ph.D. substitutes up to 3 years experience).
• Demonstrated ability to create and validate data mining methods, ML models, and analytical results via reporting and visualization.
• Strong communication skills for technical and non-technical audiences on analysis, testing, and model validation.
• U.S. Citizenship and ability to pass federal background investigation; clear pre-screening for felonies, drugs, misconduct, financial checks.

Nice to have

• Experience in financial crime, fraud detection, regulatory analytics, supply-chain, or high-stakes domains.
• Hands-on with modern NLP/LLMs: RAG, embeddings, fine-tuning, prompt engineering, evaluation.
• Graph analytics for entity resolution, network risk, link analysis.
• MLOps pipelines, feature stores, model registries, production monitoring.
• Publications, patents, or open-source ML contributions.

Culture & Benefits

• Work primarily unclassified at federal site with hybrid potential.
• Focus on responsible AI practices, emerging ML/MLOps adoption.
• Team collaboration via code reviews, mentoring, technical briefings.