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data scientist
node
java

Summary

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, Google Cloud Platform
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

ship 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
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
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

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: RTX15e75f
Position Id: 9017918
Posted 24 days ago

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Ivy Fiore

Recruiter @ Pyramid Systems, Inc.

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