Principal Data Scientist

Insulet Corporation•Acton, MA

3d•$170,600 - $255,875

About The Position

The Principal Data Scientist plays a critical role in advancing Insulet’s transformation into a data-driven organization by shaping how analytical insights inform strategic decisions across the enterprise. This position is responsible for elevating the quality, rigor, and impact of data science at scale, helping leaders make more confident decisions in areas that directly influence business performance, innovation, and operational effectiveness. As a senior technical thought leader, the Principal Data Scientist drives enterprise-wide adoption of advanced analytical approaches, establishes foundational standards that strengthen decision-making, and helps unlock greater value from Insulet’s data and AI investments to support long-term growth and improved outcomes for customers and the business. 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.

Requirements

10+ years of relevant professional experience in data science, statistics, econometrics, operations research, applied mathematics, machine learning, or a related quantitative discipline.
Demonstrated track record of providing technical leadership for complex, cross-functional analytical initiatives with material business impact.
Expert-level knowledge of statistical methodology, including experimental design, causal inference, regression, forecasting, Bayesian methods, and machine learning.
Deep proficiency in Python and modern data science libraries, including pandas, NumPy, SciPy, statsmodels, and scikit-learn.
Extensive experience developing and validating predictive models and supporting their transition into production environments.
Demonstrated ability to translate ambiguous business challenges into clear analytical frameworks, decision criteria, and defensible quantitative solutions.
Proven ability to influence senior leaders and cross-functional teams through evidence, technical credibility, and clear communication.
Strong experience conducting technical reviews, establishing analytical standards, and mentoring experienced practitioners.
Hands-on experience with Databricks or a comparable cloud-based data and analytics platform.
Exceptional written and verbal communication skills, including the ability to explain complex quantitative concepts to non-technical audiences.

Nice To Haves

Master's degree or PhD in statistics, mathematics, econometrics, operations research, computer science, engineering, or a related quantitative field.
Experience in a regulated industry such as medical devices, healthcare, pharmaceuticals, life sciences, or financial services.
Demonstrated depth in one or more areas such as experimentation, causal inference, forecasting, survival analysis, optimization, or advanced machine learning.
Experience establishing analytical standards, governance practices, reusable frameworks, or enterprise data science capabilities.
Publications, patents, conference presentations, open-source contributions, or other evidence of external or internal thought leadership.
Experience advising executive leaders on strategic decisions through quantitative evidence.
Experience working across geographically distributed, multidisciplinary teams.

Responsibilities

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.
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.
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.
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.
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.
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.
Partner with Insights & Analytics to align business context, analytical framing, interpretation, and decision adoption.
Partner with Analytics Engineering and Data Engineering to ensure analytical inputs and outputs are reproducible, governed, traceable, and accessible.
Partner with AI Engineering and MLOps to transition validated models into reliable production solutions with clear ownership and controls.
Collaborate with domain leaders and product owners to define success measures, adoption plans, and post-launch evaluation approaches.
Communicate methods, findings, limitations, and recommendations effectively to executives, business stakeholders, technical peers, and governance audiences.
Lead or contribute to cross-functional delivery forums, technical reviews, and enterprise communities that advance responsible data science.