Sr. Data Scientist

Description

Summary

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. Processes high-volume data collections and streams, making discoveries in the realm of big data. Requires strong technical and computational skills for coding, designing, and deploying sophisticated applications in unstructured data analysis. 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.

Requirements

Responsibilities

• 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.

Qualifications

• US citizenship required
• Public Trust preferred
• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or related field
• 10+ years of experience in data science, machine learning, or applied AI
• Demonstrated experience leading enterprise-scale data science initiatives
• Extensive 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
• Prior leadership role on federal programs (e.g., Lead Architect, Chief Engineer, Technical Director) preferred
• Experience with HUD or federal civilian agencies preferred
• Proven experience implementing MLOps frameworks and production ML systems (e.g., MLflow, Kubeflow, Azure ML, or SageMaker)
• Experience with big data tools (e.g., Spark) and cloud platforms (AWS, Azure, GCP
• Strong SQL skills for data extraction, transformation, and analysis
• Ability to translate ambiguous business questions into analytical solutions
• 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
• Expert-level proficiency in Python for data science and machine learning (required)
• Deep expertise in machine learning, deep learning, and LLM-based approaches
• Experience with generative AI tooling, including RAG frameworks, embedding models, and vector databases
• Strong foundation in statistics, experimentation design, and model evaluation (including precision, recall, F1 score, and related performance metrics