Senior Data Scientist

About the role

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.

Responsibilities

• 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
• Build high-quality, reusable code for data processing and feature engineering, model development and evaluation, and experimentation and statistical analysis
• 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
• Work with large-scale structured and unstructured datasets in cloud environments
• Define best practices for data preparation, feature engineering, and data quality
• Ensure alignment between data science solutions and enterprise data architecture
• Establish best practices in model validation, explainability, and interpretability
• 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

Requirements

• Expert-level proficiency in Python for data science and machine learning
• Deep expertise in machine learning, deep learning, and LLM-based approaches
• 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
• 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
• US citizenship required
• Public Trust preferred

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
• Experience establishing data science or AI capabilities in organizations early in their AI maturity
• 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
• Proven experience mentoring and leading data science teams
• Prior leadership role on federal programs (e.g., Lead Architect, Chief Engineer, Technical Director)
• Experience in AI governance, model risk management, or ethical AI
• Prior experience in consulting or client-facing environments

Skills

• Python for data science and machine learning
• Machine learning, deep learning, and LLM-based approaches
• Statistics, experimentation design, and model evaluation
• MLOps frameworks and production ML systems
• Big data tools and cloud platforms
• Data visualization and BI tools
• Federal AI governance frameworks
• Python-based data science development
• Model validation, explainability, and interpretability
• Model risk management and governance
• Data storytelling and visualization
• Data science mentoring and team leadership
• AI governance, model risk management, or ethical AI
• Consulting or client-facing experience

Benefits

Our employees value the flexibility at Pyramid Systems that allows them to balance quality work and their personal lives. We offer competitive compensation, benefits, to include our Employee Stock Ownership Program, FlexPTO, and learning and development opportunities.

Pay

USD $145,560.00/Yr. USD $210,000.00/Yr.

Schedule

N/A

Contact

Pyramid Systems, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability. Contact us for more information about our company and career opportunities.