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Principal Data Scientist
Insulet CorporationCore Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Expertise in statistical methodology, predictive modeling, and machine learning, with a strong ability to translate complex data into actionable insights. Proven leadership in establishing analytical standards and mentoring teams to drive data-driven decision-making across the enterprise.
Highest-signal resume keywords
Statistical MethodologyPredictive ModelingMachine LearningTechnical LeadershipData Science Mentoring
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Shape how data-driven decisions are made across the enterprise by transforming complex data into trusted insights
- Drive analytical rigor, predictive intelligence, and decision-making frameworks supporting growth and operational excellence
- Define analytical methodologies, standards, and best practices across the Data Science team and Enterprise Data & AI organization
- Shape the analytical roadmap and prioritize investments based on business value, feasibility, decision risk, and data readiness
- Serve as senior technical escalation point for complex statistical, experimental, modeling, and measurement questions
- Provide technical direction for major cross-functional initiatives spanning business domains, data products, or decision processes
- Lead novel, high-complexity analyses and establish standards for statistical testing, causal inference, experimentation, forecasting, and uncertainty quantification
- Define technical standards and provide oversight for predictive and machine learning models, including validation, explainability, monitoring, and lifecycle management
- Advise senior leaders on measurement strategy, experimentation, uncertainty, risk, trade-offs, and evidence-based decisions
- Establish standards for reproducible analysis, code quality, peer review, documentation, validation, and release readiness
- Lead independent technical reviews of high-impact analyses and models
- Create reusable frameworks, libraries, templates, and reference implementations
- Mentor Data Scientists and Senior Data Scientists and raise technical standards through coaching, reviews, and learning sessions
- Partner with analytics, engineering, governance, privacy, quality, compliance, business, and product teams to operationalize reliable analytical solutions
- Communicate methods, findings, limitations, and recommendations to executives, stakeholders, technical peers, and governance audiences
Requirements
What you’ll need- 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
- Master's degree or PhD in statistics, mathematics, econometrics, operations research, computer science, engineering, or a related quantitative field (preferred)
- Experience in a regulated industry such as medical devices, healthcare, pharmaceuticals, life sciences, or financial services (preferred)
- Demonstrated depth in one or more areas such as experimentation, causal inference, forecasting, survival analysis, optimization, or advanced machine learning (preferred)
- Experience establishing analytical standards, governance practices, reusable frameworks, or enterprise data science capabilities (preferred)
- Publications, patents, conference presentations, open-source contributions, or other evidence of external or internal thought leadership (preferred)
- Experience advising executive leaders on strategic decisions through quantitative evidence (preferred)
- Experience working across geographically distributed, multidisciplinary teams (preferred)
Benefits
Comp & perks- Incentive compensation eligibility
- Medical, dental, and vision insurance
- 401(k) with company match
- Paid time off (PTO)
- Additional employee wellness programs
- Hybrid working arrangements
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonExperimental DesignCausal InferenceRegressionForecastingBayesian MethodsData ValidationQuantitative AnalysisStatistical TestingOptimization
Soft Skills
Clear CommunicationInfluencing Senior LeadersTechnical CredibilityMentoringCollaboration
Tools & Technologies
DatabricksPandasNumPySciPyStatsmodelsScikit-learn
Certifications & Qualifications
Master's DegreePhD
Industry Keywords
HealthcarePharmaceuticalsLife SciencesFinancial ServicesRegulated Industry