Senior AI Data Scientist

Fully Remote Remote - Washington DC Metro Area (DMV), DC

Full-time

NOTE: This opportunity is full-time employment position only (no 1099 or C2C engagements, or third parties or staffing agencies, please). The candidate MUST be a U.S. Citizen or Permanent Resident (Green Card holder). This is a remote opportunity; candidate must be based in the U.S.; have resided in the U.S. for at least 3 years in the past 5 years; ET time zone work schedule.

• Analyzes unstructured and semi-structured data, applying creativity to large-scale analysis for high-value use cases using advanced algorithms in distributed and cloud-based infrastructures. s. Utilizes advanced tools for interpreting complex data, delivering recommendations for business decisions. Experience in software development, data transport APIs, Cloud-based tools, and visual analytics, with expertise in open-source stacks, Windows development, and various data analysis technologies.
• Execute and advance the enterprise data science and AI strategy aligned to organizational goals, serving as a trusted advisor on advanced analytics, machine learning, and AI adoption.
• Lead high-impact AI/ML initiatives across business and technology teams, delivering proofs of concept and MVPs that mature into scalable production solutions.
• Translate complex business challenges into analytical frameworks and scalable AI-driven solutions that support strategic decision-making.
• Design, develop, and deploy advanced machine learning solutions, including predictive modeling, forecasting, NLP, large language models (LLMs), recommendation systems, optimization models, RAG, and other AI-powered applications.
• Apply advanced data science techniques including deep learning, ensemble methods, time series analysis, experimentation, A/B testing, and statistical modeling.
• Lead hands-on model development in Python, establishing best practices for reusable code, testing, reproducibility, feature engineering, and utilization of modern data science frameworks and libraries.
• Partner with AI and engineering teams to implement end-to-end MLOps practices, including model versioning, automated training and deployment pipelines, monitoring, drift detection, and continuous model improvement.
• Collaborate with data engineers and architects to build scalable data platforms, pipelines, and cloud-based solutions that support large-scale structured and unstructured data.
• Establish and enforce standards for model validation, explainability, interpretability, data quality, governance, responsible AI, bias mitigation, transparency, and auditability.
• Communicate complex analytical insights to executive and non-technical stakeholders through effective data storytelling, visualization, and strategic recommendations.
• Mentor and develop data science talent while leading ross-functional teams to deliver high-impact data science and AI solutions.

• Must of a U.S. Citizen or Permanent Resident (Green Card holder), as mandated by our government client.
• Must be able to complete/pass/hold at a minimum a Public Trust Investigation / background check. An active Public Trust or higher is preferred.
• Must be based / reside in the U.S.
• 10+ years of experience in data science, machine learning, or applied AI with deep expertise in machine learning, deep learning, and LLM-based approaches.
• 6+ years of demonstrated experience leading enterprise-scale data science initiatives.
• Expert-level proficiency in Python for data science and machine learning, including hands-on Python experience delivering production-grade data science solutions.
• Proven experience building and deploying ML models in production environments.
• Strong experience with MLOps tools, pipelines, and lifecycle management.
• Experience with LLMs, NLP, or generative AI applications.
• Experience in AI governance, model risk management, or ethical AI.
• Proven experience implementing MLOps frameworks and production ML systems (e.g., MLflow, Kubeflow, Azure ML, or Sage Maker).
• Experience with big data tools (e.g., Spark) and cloud platforms (AWS, Azure, GCP).
• Strong SQL skills for data extraction, transformation, and analysis.
• Proficiency with data visualization and BI tools (e.g., Power BI, Tableau).
• Familiarity with federal AI governance frameworks, including the NIST AI Risk Management Framework and OMB AI guidance.
• Experience working with sensitive data, including PII safeguards such as anonymization, masking, and data loss prevention.
• Experience with generative AI…