<p>Position Summary</p> <p>The Principal Data Scientist is a senior individual contributor responsible for technical leadership, modeling rigor, and methodological excellence across the AI & Data Science organization. This role serves as the organization's highest-level data science authority, setting standards for statistical modeling, machine learning, experimentation, and responsible AI practices in a production environment.</p> <p>The Principal Data Scientist combines organizational-level ownership of data science methodology with hands-on contribution to high-priority model development, guiding and enabling data scientists across cross-functional teams to deliver rigorous, production-ready models. This position partners closely with delivery leadership and hands-on practitioners across the Data and AI organization to ensure data science work is technically sound, scalable, and aligned to business outcomes.</p> <p>Primary Duties & Responsibilities</p> <ul> <li>Define and maintain data science and machine learning standards across the organization, including modeling methodologies, statistical rigor, validation approaches, documentation requirements, and best practices for production ML systems. </li><li>Provide technical design leadership and peer review for complex, high-impact, or novel modeling efforts, including model selection, feature engineering strategies, evaluation metrics, and tradeoff analysis. </li><li>Ensure modeling quality and statistical validity by establishing expectations for experimentation design, bias and variance analysis, robustness testing, and appropriate use of statistical and machine learning techniques. </li><li>Establish and evolve experimentation, validation, and monitoring frameworks to support reproducible model development, ongoing performance tracking, and lifecycle management in production environments. </li><li>Champion responsible AI practices, including fairness, explainability, transparency, and governance, and ensure these considerations are embedded into model design, evaluation, and deployment workflows. </li><li>Advise product and delivery leadership on technical feasibility and risk, helping teams make informed decisions about modeling approaches, timelines, and expected outcomes. </li><li>Mentor and coach senior and mid-level data scientists through technical guidance, design and code reviews, and team discussions. </li><li>Facilitating knowledge sharing, technical forums, and cross-team alignment on tools, techniques, and modeling approaches, while partnering with line managers and product owners on capability development. </li><li>Drive reuse and consistency by promoting shared modeling patterns, feature frameworks, evaluation templates, and reference implementations across teams. </li></ul> <p>Qualifications</p> <ul> <li>Advanced degree (Master's or PhD preferred) in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related quantitative field, or equivalent practical experience. </li><li>10+ years of experience in data science, applied machine learning, or statistical modeling roles, with demonstrated impact in production environments. </li><li>Deep expertise in statistical modeling, machine learning, and experimentation, including both classical and modern approaches. </li><li>Strong experience designing and evaluating predictive, risk, or outcome models, including feature engineering and model interpretation. </li><li>Proven ability to influence technical direction across multiple teams without direct authority. </li><li>Experience partnering closely with engineering teams to deploy, monitor, and iterate on models in production. </li><li>Familiarity with responsible AI principles and practical implementation of fairness, explainability, and governance. </li><li>Experience with cloud-based data and ML platforms (e.g., Azure) is a plus but not required. </li></ul>