Senior Data Scientist

Overview:

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. The role emphasizes deep Python-based modeling expertise, leadership of end-to-end ML lifecycle and MLOps, and delivery of scalable AI solutions (including large language models) that drive measurable business and mission outcomes for HUD programs (e.g., housing analytics, fraud detection, and intelligent document processing).

Responsibilities:

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 solution

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

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

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