Principal Data Scientist

Insulet Corporation•Acton, MA

2d•$170,600 - $255,875•Hybrid

About The Position

The Principal Data Scientist is a senior technical leader within Insulet’s Data Science team in the Enterprise Data & AI organization. This individual shapes analytical standards and technical direction for strategically important initiatives, applying expertise in statistics, experimentation, causal inference, forecasting, machine learning, and quantitative decision science to complex business problems. The role remains hands-on for novel, high-risk, or enterprise-critical work while extending impact through technical direction, reusable methods, reviews, mentorship, and cross-functional influence. It is an individual-contributor position with enterprise-wide influence, and success is measured by the decisions improved, standards established, capabilities developed, and analytical quality raised across teams. The Principal Data Scientist partners with Insights & Analytics, Analytics Engineering, AI Engineering, Data Engineering, governance teams, business leaders, and other stakeholders. The position translates ambiguous questions into defensible analytical programs and measurable outcomes, advises leaders on evidence and uncertainty, and helps operationalize validated models and analyses. The role also raises analytical rigor through standards for methodology, reproducibility, validation, documentation, governance, and communication. This 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; the role may also be eligible for incentive compensation. Actual pay depends on skills, experience, and education. The posting remains open until the position is filled.

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 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 across the Data Science team and broader Enterprise Data & AI organization.
Shape the analytical roadmap by identifying high-value opportunities 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 and represent Data Science in portfolio, architecture, governance, and strategic planning forums.
Lead complex analyses and 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 and enterprise experimentation frameworks.
Lead advanced applications of time-series analysis, forecasting, anomaly detection, survival analysis, simulation, segmentation, and optimization; select an appropriate level of methodological complexity for each problem.
Define technical standards and provide oversight for predictive and machine-learning model design, validation, explainability, monitoring, and lifecycle management.
Guide feature engineering, model selection, regularization, cross-validation, calibration, interpretability, fairness assessment, and robustness testing.
Guide model monitoring, drift detection, retraining triggers, revalidation, and retirement criteria with AI Engineering and MLOps; ensure production-bound models have documented methodology, validation evidence, benchmarks, limitations, and failure modes.
Partner with senior leaders and domain experts to frame ambiguous challenges as 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.
Synthesize analyses into clear decision-oriented narratives, connecting findings to practical actions, expected business impact, and measurable follow-through without overstating certainty.
Establish standards for reproducible analysis, code quality, peer review, documentation, validation, and release readiness; conduct independent technical reviews of high-impact analyses and models.
Define documentation requirements and promote transparent reporting of assumptions, data lineage, limitations, uncertainty, bias, 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, design and code reviews, learning sessions, and communities of practice.
Develop reference methods and training content, influence hiring and technical assessment practices, and foster scientific rigor, constructive challenge, intellectual honesty, collaboration, and continuous learning.
Partner with Insights & Analytics, Analytics Engineering, Data Engineering, AI Engineering, MLOps, domain leaders, and product owners to support reproducible and governed data, production transition, success measures, adoption, and post-launch evaluation.
Communicate methods, findings, limitations, and recommendations to executives, business stakeholders, technical peers, and governance audiences; lead or contribute to cross-functional delivery forums and technical reviews.