Principal Data Scientist

Berkley•Glen Allen, VA

22h

About The Position

The Principal Data Scientist is a senior individual contributor and thought leader responsible for defining, advancing, and scaling enterprise data science capabilities. This role sits at the intersection of advanced analytics, business strategy, and technology, driving high-impact solutions that influence decision-making across underwriting, claims, operations, finance, and other enterprise functions. This position is designed for a seasoned data science professional who combines deep technical expertise with strong business acumen and leadership influence—mentoring teams, shaping standards, and guiding the end-to-end lifecycle of advanced analytic products without direct people management responsibility. Strategic Leadership & Influence Serve as a principal-level advisor on advanced analytics and data science strategy across the enterprise. Partner with senior business and technology leaders to identify, prioritize, and frame high-value analytic opportunities. Translate ambiguous business problems into well-defined data science initiatives with measurable outcomes. Influence enterprise standards for model development, validation, deployment, and lifecycle management. Advanced Analytics, AI & Data Science Design, develop, and oversee advanced statistical, machine learning, and predictive modeling solutions. Lead complex analytical efforts including feature engineering, model selection, evaluation, and interpretability. Ensure analytical rigor, reproducibility, and alignment with enterprise data governance and risk standards. Guide model monitoring, retraining, and performance optimization strategies in production environments. Architecture, Platforms & Enablement Collaborate with data engineering, architecture, and platform teams to ensure scalable, production-ready solutions. Evaluate and recommend tools, frameworks, and cloud-based platforms supporting data science workloads. Establish reusable patterns, accelerators, and best practices that elevate team productivity and consistency. Mentorship & Community Leadership Act as a technical mentor and coach to data scientists, analysts, and cross-functional partners. Elevate analytical maturity by sharing best practices, conducting design reviews, and leading knowledge-sharing forums. Contribute to talent development through hiring support, onboarding guidance, and capability roadmaps. Communication & Executive Engagement Communicate complex analytical concepts clearly to technical and non-technical audiences. Present insights, recommendations, and trade-offs to senior and executive stakeholders. Champion data-driven decision-making through clear storytelling and executive-ready deliverables.

Requirements

Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related field (Master’s or PhD preferred).
10+ years of progressive experience in advanced analytics, data science, or applied machine learning.
Demonstrated expertise in statistical modeling, machine learning, and predictive analytics.
Strong proficiency in Python, SQL, and modern data science libraries and frameworks.
Experience deploying and supporting models in production environments.
Proven ability to influence without authority and lead through expertise.
Strong business acumen with experience applying analytics to real-world decision-making.

Nice To Haves

Experience in financial services, insurance, or other regulated industries.
Familiarity with cloud-based analytics platforms (e.g., Azure).
Experience with MLOps, model governance, and monitoring frameworks.
Track record of shaping enterprise analytics standards or centers of excellence.
Experience working in agile or product-oriented analytics environments.

Responsibilities

Serve as a principal-level advisor on advanced analytics and data science strategy across the enterprise.
Partner with senior business and technology leaders to identify, prioritize, and frame high-value analytic opportunities.
Translate ambiguous business problems into well-defined data science initiatives with measurable outcomes.
Influence enterprise standards for model development, validation, deployment, and lifecycle management.
Design, develop, and oversee advanced statistical, machine learning, and predictive modeling solutions.
Lead complex analytical efforts including feature engineering, model selection, evaluation, and interpretability.
Ensure analytical rigor, reproducibility, and alignment with enterprise data governance and risk standards.
Guide model monitoring, retraining, and performance optimization strategies in production environments.
Collaborate with data engineering, architecture, and platform teams to ensure scalable, production-ready solutions.
Evaluate and recommend tools, frameworks, and cloud-based platforms supporting data science workloads.
Establish reusable patterns, accelerators, and best practices that elevate team productivity and consistency.
Act as a technical mentor and coach to data scientists, analysts, and cross-functional partners.
Elevate analytical maturity by sharing best practices, conducting design reviews, and leading knowledge-sharing forums.
Contribute to talent development through hiring support, onboarding guidance, and capability roadmaps.
Communicate complex analytical concepts clearly to technical and non-technical audiences.
Present insights, recommendations, and trade-offs to senior and executive stakeholders.
Champion data-driven decision-making through clear storytelling and executive-ready deliverables.