Senior Data Scientist

Senior Data Scientist

Job Location s: US
Job :
Number of Openings: 1

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).

• 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, Num Py, Scikit‑learn, PyTorch/Tensor Flow
• 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 product ionise 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
• 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 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
• Ability to…