Principal Data Scientist

Job Description Summary

The Principal Data Scientist is a senior technical leader responsible for defining and executing enterprise-wide Artificial Intelligence / Machine Learning strategy across the Power Data Science & Artificial Intelligent (DS&AI) organization. This role elevates the impact of the data science function by establishing best-in-class standards for modeling, experimentation, and robust algorithm design, while mentoring and guiding teams toward high-quality, production-ready outcomes.
As a hands-on leader, this role architects and delivers accurate, robust, and production-grade data science, GenAI, and ML algorithms and methods that drive measurable business value and accelerate digital transformation initiatives.
The Principal Data Scientist collaborates closely with business, engineering, product, and solution architecture teams to integrate advanced analytics, ML, and GenAI capabilities into core workflows, products, and enterprise decision-making. This role combines deep technical expertise with strong business acumen to lead complex, cross-functional initiatives from ideation through scalable, enterprise-wide adoption.

Job Description

Roles and Responsibilities

• Serves as a senior technical and strategic leader defining and executing enterprise-wide AI/ML and GenAI algorithms and methods aligned with Power business priorities and digital transformation goals.
• Elevates the impact of the data science organization by establishing best-in-class standards, accurate & robust solutions, and driving measurable business value through advanced analytics.
• Defines and enforces standards for modeling, exploratory data analysis (EDA), experimentation, evaluation, and responsible AI.
• Establishes best practices for reproducibility, versioning, validation, and model lifecycle management.
• Sets high expectations for code quality, documentation, and scalable solution design across the data science community.
• Leads technical design reviews and governance for high-impact AI/ML and GenAI initiatives.
• Ensures solutions balance technical rigor with scalability, maintainability, operational feasibility, and measurable business value.
• Drives continuous model performance improvements through structured experimentation, feature engineering, model selection, tuning, and monitoring.
• Guides teams in selecting appropriate methodologies, architectures, and tools for complex, ambiguous problem spaces.
• Drives a culture of scientific rigor by establishing clear hypotheses, baselines, and success metrics.
• Standardizes experimentation practices to ensure results are reproducible, comparable, and decision-ready.
• Partners with business, engineering, product, and solution architecture teams to embed advanced analytics, machine learning, and GenAI into core workflows, products, and decision-making.
• Shapes enterprise data science capabilities, including reusable assets, accelerators, and scalable patterns such as GenAI and RAG based solutions.
• Influences platform, tooling, and architectural decisions in collaboration with data and engineering teams.
• Partners with data engineering and domain teams to ensure fit-for-purpose data across structured and unstructured sources.
• Embeds EDA and data readiness as foundational steps in the AI development lifecycle.
• Establishes and enforces standards for model governance, monitoring, auditability, and risk management.
• Identifies and mitigates risks related to model deployment, data dependencies, and operational performance.
• Acts as a thought leader on emerging AI/ML and GenAI trends, translating innovation into practical enterprise applications.
• Incubates new approaches, pilots emerging capabilities,
• Mentors and develops data scientists, strengthening technical depth and leadership capability.
• Fosters a culture of innovation, collaboration, and continuous learning within the organization.
• Enables teams to operate independently by providing clear standards, frameworks, and decision guidelines.

• Master's degree or PhD in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering, Operations Research, or a related quantitative field.
• 10+ years of experience in data science, machine learning, advanced analytics, or AI solution development, with a significant portion in enterprise or industrial environments.
• Proven track record of leading large-scale, high-impact AI/ML initiatives from strategy through deployment and business adoption.
• Deep expertise in machine learning, statistical modeling, optimization, and data-driven decision systems.
• Strong experience developing and deploying production-grade AI/ML solutions in collaboration with engineering and product teams.
• Demonstrated ability to influence senior leaders and work effectively across complex, matrixed organizations.
• Experience mentoring technical teams and leading through influence rather than direct authority.

• Strong oral and written communication skills. Strong interpersonal and leadership skills. Demonstrated ability to analyze and resolve problems. Demonstrated ability to lead programs / projects. Ability to document, plan, market, and execute programs. Established project management skills.