Principal Data Scientist (Hybrid)

Job Summary

• Job Description Summary
• As a Principal Data Scientist at Insulet, you'll help shape how data-driven decisions are made across the enterprise by transforming complex data into trusted insights that influence strategy, innovation, and business performance. This highly visible role sits at the center of Insulet's AI and Data transformation, driving the analytical rigor, predictive intelligence, and decision-making frameworks that support continued growth and operational excellence. You'll have the opportunity to solve meaningful business challenges at scale while helping build the future of data science capabilities across a company dedicated to improving lives through diabetes technology.

 Role Overview

The Principal Data Scientist is a senior technical leader within the Data Science team in the Enterprise Data & AI organization. The role defines analytical standards, shapes the technical direction of strategically important initiatives, and applies deep expertise in statistics, experimentation, causal inference, forecasting, machine learning, and quantitative decision science to the most complex business problems.

This individual remains hands-on where the work is novel, high-risk, or enterprise-critical, while extending impact through technical direction, reusable methods, rigorous review, mentorship, and cross-functional influence. The Principal Data Scientist partners with Insights & Analytics, Analytics Engineering, AI Engineering, Data Engineering, governance teams, and business leaders to convert ambiguous questions into defensible analytical programs and measurable outcomes.

This is an individual contributor role with enterprise-wide influence. Success is measured not only by the quality of personally delivered work, but also by the decisions improved, standards established, capabilities developed, and analytical quality raised across teams.

Scope of Responsibility

Analytical Strategy & Enterprise Technical Leadership

• •Define and evolve analytical methodologies, standards, and best practices used across the Data Science team and broader Enterprise Data & AI organization.
• •Shape the analytical roadmap by identifying high-value opportunities, clarifying where advanced methods are warranted, and helping prioritize investments based on business value, feasibility, decision risk, and data readiness.
• •Serve as the senior technical escalation point for complex statistical, experimental, modeling, and measurement questions.
• •Provide technical direction for major cross-functional initiatives that span multiple business domains, data products, or decision processes.
• •Evaluate emerging quantitative methods and technologies, determine their enterprise relevance, and guide responsible adoption.
• •Represent Data Science in portfolio, architecture, governance, and strategic planning forums where analytical methodology or decision quality is material.

Advanced Statistical Analysis & Quantitative Methods

• •Lead the design and execution of novel, high-complexity analyses supporting strategic and high-consequence decisions. Establish standards for hypothesis testing, power analysis, confidence intervals, effect-size reporting, multiple-comparison control, sensitivity analysis, and uncertainty quantification.
• •Define and guide causal inference approaches for observational data, including difference-in-differences, regression discontinuity, propensity score methods, instrumental variables, and synthetic controls.
• •Design enterprise experimentation frameworks covering randomization, sample-size determination, holdouts, guardrail metrics, heterogeneous treatment effects, and interpretation of results.
• •Lead advanced applications of time-series analysis, forecasting, anomaly detection, survival analysis, simulation, segmentation, and optimization.
• •Determine the appropriate level of methodological complexity for each problem and prevent unnecessary modeling when simpler approaches are more reliable or actionable.
• •Review and approve analytical approaches for strategically significant or methodologically high-risk initiatives.

Predictive Modeling & Machine Learning Leadership

• •Define technical standards for the design, validation, explainability, monitoring, and lifecycle management of predictive and machine learning models.
• •Provide technical oversight for critical models, including propensity, attrition, forecasting, anomaly detection, classification, regression, and optimization solutions.
• •Guide feature engineering, model selection, regularization, cross-validation, calibration, interpretability, fairness assessment, and robustness testing.
• •Establish performance evaluation practices that balance accuracy, calibration, stability, explainability, business utility, and operational feasibility.
• •Guide model monitoring, drift detection, retraining triggers, revalidation, and retirement criteria in partnership with AI Engineering and MLOps.
• •Ensure production-bound models are accompanied by clear methodology, validation evidence, performance benchmarks, limitations, and known failure modes.

Business Problem Framing & Executive Advisory

• •Partner with senior leaders and domain experts to translate ambiguous strategic challenges into well-structured analytical programs with explicit decisions, hypotheses, success measures, and value expectations.
• •Advise leaders on measurement strategy, experimentation opportunities, uncertainty, risk, and trade-offs so that decisions are based on appropriate evidence.
• •Challenge unsupported assumptions and identify when available data or study design cannot reliably answer a question.
• •Recommend alternative measurement, data collection, or analytical strategies when existing evidence is insufficient.
• •Synthesize complex analyses into clear, decision-oriented narratives without overstating certainty or obscuring limitations.
• •Connect analytical findings to practical actions, expected business impact, and measurable follow-through.

Analytical Quality, Governance & Reproducibility

• •Establish and maintain standards for reproducible analysis, code quality, peer review, documentation, validation, and release readiness.
• •Lead independent technical reviews of high-impact analyses and models to ensure methodological soundness, traceability, and defensibility.
• •Define minimum documentation requirements for analytical methods, assumptions, data lineage, validation, limitations, sensitivity analyses, and conclusions.
• •Promote transparent reporting of uncertainty, bias, data limitations, model risk, and alternative interpretations.
• •Create reusable frameworks, libraries, templates, and reference implementations that improve consistency, speed, and quality across teams.
• •Partner with data governance, privacy, quality, and compliance stakeholders to ensure analytical work follows applicable enterprise requirements.
• •Ensure deliverables meet established Data Science and data product quality standards before release.

Capability Building & Mentorship

• •Mentor Data Scientists and Senior Data Scientists on problem framing, methodology selection, analytical design, technical communication, and career development.
• •Raise the technical bar through coaching, design reviews, code reviews, learning sessions, and communities of practice.
• •Develop reference methods and training content in experimentation, causal inference, forecasting, machine learning, and statistical reasoning.
• •Influence role expectations, hiring standards, interview practices, and technical assessment criteria for data science talent.
• •Build a culture of scientific rigor, constructive challenge, intellectual honesty, collaboration, and continuous learning.
• •Provide leadership through influence across teams without relying on direct reporting authority.

Cross-Functional Partnership & Operationalization

Partner with Insights & Analytics to align business context, analytical framing, interpretation, and decision adoption.