Principal Data Scientist
- Compensation
- Not disclosed
- Location
- US - Massachusetts
- Work setup
- Hybrid
- Seniority
- Principal
- Employment
- Not specified
- Experience
- 10+ years
- Category
- Data science
- Posted
Skills
Must have
- 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 have
- 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.
Full description
Job 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 ResponsibilityAnalytical 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.