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