Senior Data Scientist

Role Description

The Senior Data Scientist is a senior technical leader responsible for executing and advancing the advanced analytics, machine learning, and AI strategy across the organization. This role focuses on applied data science at enterprise scale, including model development, experimentation, and operationalization.

• Execute and advance the enterprise data science and AI strategy aligned to organizational goals
• Serve as a trusted advisor on advanced analytics, machine learning, and AI adoption
• Lead high-impact AI/ML initiatives across business and technology teams
• Deliver time-boxed proofs of concept and MVP solutions that establish foundational AI capabilities and mature into production systems
• Translate complex business problems into analytical frameworks and scalable solutions
• Design, develop, and deploy advanced machine learning models, including predictive modeling and forecasting, NLP and large language models (LLMs), and recommendation systems and optimization models
• Apply advanced techniques such as deep learning, ensemble methods, and time series analysis
• Develop and scale modern AI solutions including Retrieval-Augmented Generation (RAG) and LLM-based workflows and applications
• Ensure models are robust, explainable, and production-ready
• Lead hands-on model development using Python as the primary programming language
• Build high-quality, reusable code for data processing and feature engineering, model development and evaluation, and experimentation and statistical analysis
• Establish best practices for Python-based data science development, including code quality, testing, and reproducibility
• Utilize core libraries such as Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow
• Partner with the Senior AI Engineer to operationalize end-to-end MLOps practices, including model versioning, tracking, and reproducibility, automated training and deployment pipelines, model monitoring, drift detection, and performance management
• Ensure continuous delivery and improvement of models in production
• Partner with engineering teams to productionize models while maintaining data science ownership of model integrity
• Establish standards for experimentation, A/B testing, and model validation
• Partner with data engineers and architects to build scalable data pipelines and platforms
• Define best practices for data preparation, feature engineering, and data quality
• Work with large-scale structured and unstructured datasets in cloud environments
• Ensure alignment between data science solutions and enterprise data architecture
• Establish best practices in model validation, explainability, and interpretability
• Ensure responsible AI practices including bias detection and mitigation
• Support model risk management and governance frameworks
• Promote transparency and auditability in AI/ML systems
• Communicate complex analytical insights to executive and non-technical stakeholders
• Influence decision-making through data storytelling and visualization
• Mentor and develop data scientists and analysts
• Lead cross-functional teams delivering high-impact data science solutions

Qualifications

• 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
• Experience establishing data science or AI capabilities in organizations early in their AI maturity preferred
• 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 mentoring and leading data science teams
• Experience in AI governance, model risk management, or ethical AI
• U.S. citizenship required; active clearance (Public Trust, Secret, or higher) preferred
• Prior leadership role on federal programs (e.g., Lead Architect, Chief Engineer, Technical Director) preferred
• Experience with HUD or federal civilian agencies preferred
• Prior experience in consulting or client-facing environments preferred

Requirements

• 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)
• 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
• Strong communication and stakeholder engagement skills
• 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

Benefits

• Competitive compensation
• Employee Stock Ownership Program
• FlexPTO
• Learning and development opportunities