Principal Data Scientist

Role Overview

• 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.
• 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.
• 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.
• Establish and evolve experimentation, validation, and monitoring frameworks to support reproducible model development, ongoing performance tracking, and lifecycle management in production environments.
• Champion responsible AI practices, including fairness, explainability, transparency, and governance, and ensure these considerations are embedded into model design, evaluation, and deployment workflows.
• Advise product and delivery leadership on technical feasibility and risk, helping teams make informed decisions about modeling approaches, timelines, and expected outcomes.
• Mentor and coach senior and mid-level data scientists through technical guidance, design and code reviews, and team discussions.
• 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.
• Drive reuse and consistency by promoting shared modeling patterns, feature frameworks, evaluation templates, and reference implementations across teams.

Requirements

• Advanced degree (Master’s or PhD preferred) in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related quantitative field, or equivalent practical experience.
• 10+ years of experience in data science, applied machine learning, or statistical modeling roles, with demonstrated impact in production environments.
• Deep expertise in statistical modeling, machine learning, and experimentation, including both classical and modern approaches.
• Strong experience designing and evaluating predictive, risk, or outcome models, including feature engineering and model interpretation.
• Proven ability to influence technical direction across multiple teams without direct authority.
• Experience partnering closely with engineering teams to deploy, monitor, and iterate on models in production.
• Familiarity with responsible AI principles and practical implementation of fairness, explainability, and governance.
• Experience with cloud-based data and ML platforms (e.g., Azure) is a plus but not required.

Tech Stack

• Azure
• Cloud