Principal Data Scientist (Hybrid)

Insulet Corporation•Acton, MA

4d•$170,600 - $255,875•Hybrid

About The Position

The Principal Data Scientist is a senior technical leader in Insulet’s Data Science team within the Enterprise Data & AI organization. This individual contributor role has enterprise-wide influence and shapes analytical standards, technical direction, and decision-making frameworks. The role applies advanced expertise in statistics, experimentation, causal inference, forecasting, machine learning, and quantitative decision science to complex business problems, remaining hands-on for novel, high-risk, or enterprise-critical work while extending impact through technical direction, reusable methods, reviews, mentorship, and cross-functional influence. The Principal Data Scientist partners with teams across Insights & Analytics, Analytics Engineering, AI Engineering, Data Engineering, governance, and business leadership. The role translates ambiguous questions into defensible analytical programs and measurable outcomes, advises leaders on evidence and uncertainty, and raises analytical quality across the organization. Success is measured by the quality of delivered work as well as improved decisions, established standards, developed capabilities, and stronger analytical practices. The position is eligible for hybrid working arrangements and requires on-site work from an Insulet office. For U.S.-based positions, the annual base salary range is $170,600.00–$255,875.00, with possible incentive compensation. The posting remains open until the position is filled. Insulet describes itself as a medical device company focused on diabetes technology and other therapeutic areas.

Requirements

10 or more years of relevant professional experience in data science, statistics, econometrics, operations research, applied mathematics, machine learning, or a related quantitative discipline.
Demonstrated technical leadership of 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.
Ability to translate ambiguous business challenges into clear analytical frameworks, decision criteria, and defensible quantitative solutions.
Ability to influence senior leaders and cross-functional teams through evidence, technical credibility, and clear communication.
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.
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 across Data Science and the Enterprise Data & AI organization.
Shape the analytical roadmap by identifying high-value opportunities and prioritizing investments according to business value, feasibility, decision risk, and data readiness.
Serve as a senior technical escalation point for complex statistical, experimental, modeling, and measurement questions.
Provide technical direction for major cross-functional initiatives and represent Data Science in portfolio, architecture, governance, and strategic-planning forums.
Evaluate emerging quantitative methods and technologies and guide their responsible adoption.
Lead novel, high-complexity analyses and establish standards for hypothesis testing, power analysis, confidence intervals, effect-size reporting, multiple-comparison control, sensitivity analysis, and uncertainty quantification.
Define causal inference approaches for observational data and design enterprise experimentation frameworks.
Lead advanced applications of time-series analysis, forecasting, anomaly detection, survival analysis, simulation, segmentation, and optimization.
Select appropriate methodological complexity and review or approve approaches for strategically significant or methodologically high-risk initiatives.
Define technical standards for predictive and machine-learning model design, validation, explainability, monitoring, and lifecycle management.
Provide technical oversight for critical models and guide feature engineering, model selection, regularization, cross-validation, calibration, interpretability, fairness assessment, and robustness testing.
Establish model-performance evaluation practices and guide monitoring, drift detection, retraining, revalidation, and retirement criteria with AI Engineering and MLOps.
Ensure production-bound models include methodology, validation evidence, benchmarks, limitations, and known failure modes.
Partner with leaders and domain experts to turn strategic challenges into analytical programs with explicit decisions, hypotheses, success measures, and value expectations.
Advise leaders on measurement strategy, experimentation, uncertainty, risk, and trade-offs; challenge unsupported assumptions and recommend alternative approaches when evidence is insufficient.
Communicate complex analyses as decision-oriented narratives, connecting findings to practical actions, expected impact, and measurable follow-through without overstating certainty.
Establish standards for reproducible analysis, code quality, peer review, documentation, validation, and release readiness.
Lead independent technical reviews of high-impact analyses and models; define documentation requirements and promote transparent reporting of uncertainty, bias, data limitations, model risk, and alternative interpretations.
Create reusable frameworks, libraries, templates, and reference implementations; partner with governance, privacy, quality, and compliance stakeholders to meet enterprise requirements.
Mentor Data Scientists and Senior Data Scientists, raise technical standards through coaching and reviews, and develop training and reference methods.
Influence hiring standards, interview practices, and technical assessment criteria; foster scientific rigor, intellectual honesty, collaboration, and continuous learning.
Partner with analytics and engineering teams to make analytical inputs and outputs reproducible, governed, traceable, accessible, and suitable for production.
Collaborate with domain leaders and product owners on success measures, adoption plans, and post-launch evaluation; communicate with executive, business, technical, and governance audiences.
Lead or contribute to cross-functional delivery forums, technical reviews, and enterprise communities that advance responsible data science.