{"id":3032460,"url":"https://alion.io/job/insulet-principal-data-scientist-hybrid","title":"Principal Data Scientist (Hybrid)","company":{"id":5958,"name":"Insulet","domain":"insulet.com","url":"https://alion.io/company/insulet","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":93,"open_postings":27,"ghost_share":0,"stale_share":0.222,"repost_share":0.111,"time_to_fill_p50_days":28,"computed_at":"2026-10-26T05:57:25Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":170600,"max":255875,"currency":"USD","period":"year","gross":null,"usd_annual":255875},"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Databricks","optional":false},{"name":"Interpretability","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"Statsmodels","optional":false},{"name":"Time Series","optional":false}],"status":"live","first_seen_at":"2026-10-21T20:37:52Z","employer_posted_date":"2026-10-21","last_verified_at":"2026-10-25T07:37:31Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"Job Summary\nJob Description SummaryAs 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.\n\nRole Overview\nThe 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.\nThis 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.\nThis 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.\nScope of Responsibility\nAnalytical Strategy & Enterprise Technical Leadership\n\nDefine and evolve analytical methodologies, standards, and best practices used across the Data Science team and broader Enterprise Data & AI organization.\nShape 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.\nServe as the senior technical escalation point for complex statistical, experimental, modeling, and measurement questions.\nProvide technical direction for major cross-functional initiatives that span multiple business domains, data products, or decision processes.\nEvaluate emerging quantitative methods and technologies, determine their enterprise relevance, and guide responsible adoption.\nRepresent Data Science in portfolio, architecture, governance, and strategic planning forums where analytical methodology or decision quality is material.\nAdvanced Statistical Analysis & Quantitative Methods\n\nLead 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.\nDefine and guide causal inference approaches for observational data, including difference-in-differences, regression discontinuity, propensity score methods, instrumental variables, and synthetic controls.\nDesign enterprise experimentation frameworks covering randomization, sample-size determination, holdouts, guardrail metrics, heterogeneous treatment effects, and interpretation of results.\nLead advanced applications of time-series analysis, forecasting, anomaly detection, survival analysis, simulation, segmentation, and optimization.\nDetermine the appropriate level of methodological complexity for each problem and prevent unnecessary modeling when simpler approaches are more reliable or actionable.\nReview and approve analytical approaches for strategically significant or methodologically high-risk initiatives.\nPredictive Modeling & Machine Learning Leadership\n\nDefine technical standards for the design, validation, explainability, monitoring, and lifecycle management of predictive and machine learning models.\nProvide technical oversight for critical models, including propensity, attrition, forecasting, anomaly detection, classification, regression, and optimization solutions.\nGuide feature engineering, model selection, regularization, cross-validation, calibration, interpretability, fairness assessment, and robustness testing.\nEstablish performance evaluation practices that balance accuracy, calibration, stability, explainability, business utility, and operational feasibility.\nGuide model monitoring, drift detection, retraining triggers, revalidation, and retirement criteria in partnership with AI Engineering and MLOps.\nEnsure production-bound models are accompanied by clear methodology, validation evidence, performance benchmarks, limitations, and known failure modes.\nBusiness Problem Framing & Executive Advisory\n\nPartner 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.\nAdvise leaders on measurement strategy, experimentation opportunities, uncertainty, risk, and trade-offs so that decisions are based on appropriate evidence.\nChallenge unsupported assumptions and identify when available data or study design cannot reliably answer a question.\nRecommend alternative measurement, data collection, or analytical strategies when existing evidence is insufficient.\nSynthesize complex analyses into clear, decision-oriented narratives without overstating certainty or obscuring limitations.\nConnect analytical findings to practical actions, expected business impact, and measurable follow-through.\nAnalytical Quality, Governance & Reproducibility\n\nEstablish and maintain standards for reproducible analysis, code quality, peer review, documentation, validation, and release readiness.\nLead independent technical reviews of high-impact analyses and models to ensure methodological soundness, traceability, and defensibility.\nDefine minimum documentation requirements for analytical methods, assumptions, data lineage, validation, limitations, sensitivity analyses, and conclusions.\nPromote transparent reporting of uncertainty, bias, data limitations, model risk, and alternative interpretations.\nCreate reusable frameworks, libraries, templates, and reference implementations that improve consistency, speed, and quality across teams.\nPartner with data governance, privacy, quality, and compliance stakeholders to ensure analytical work follows applicable enterprise requirements.\nEnsure deliverables meet established Data Science and data product quality standards before release.\nCapability Building & Mentorship\n\nMentor Data Scientists and Senior Data Scientists on problem framing, methodology selection, analytical design, technical communication, and career development.\nRaise the technical bar through coaching, design reviews, code reviews, learning sessions, and communities of practice.\nDevelop reference methods and training content in experimentation, causal inference, forecasting, machine learning, and statistical reasoning.\nInfluence role expectations, hiring standards, interview practices, and technical assessment criteria for data science talent.\nBuild a culture of scientific rigor, constructive challenge, intellectual honesty, collaboration, and continuous learning.\nProvide leadership through influence across teams without relying on direct reporting authority.\nCross-Functional Partnership & Operationalization\n\nPartner with Insights & Analytics to align business context, analytical framing, interpretation, and decision adoption.\nPartner with Analytics Engineering and Data Engineering to ensure analytical inputs and outputs are reproducible, governed, traceable, and accessible.\nPartner with AI Engineering and MLOps to transition validated models into reliable production solutions with clear ownership and controls.\nCollaborate with domain leaders and product owners to define success measures, adoption plans, and post-launch evaluation approaches.\nCommunicate methods, findings, limitations, and recommendations effectively to executives, business stakeholders, technical peers, and governance audiences.\nLead or contribute to cross-functional delivery forums, technical reviews, and enterprise communities that advance responsible data science.\nRequired Qualifications\n\n10+ years of relevant professional experience in data science, statistics, econometrics, operations research, applied mathematics, machine learning, or a related quantitative discipline.\nDemonstrated track record of providing technical leadership for complex, cross-functional analytical initiatives with material business impact.\nExpert-level knowledge of statistical methodology, including experimental design, causal inference, regression, forecasting, Bayesian methods, and machine learning.\nDeep proficiency in Python and modern data science libraries, including pandas, NumPy, SciPy, statsmodels, and scikit-learn.\nExtensive experience developing and validating predictive models and supporting their transition into production environments.\nDemonstrated ability to translate ambiguous business challenges into clear analytical frameworks, decision criteria, and defensible quantitative solutions.\nProven ability to influence senior leaders and cross-functional teams through evidence, technical credibility, and clear communication.\nStrong experience conducting technical reviews, establishing analytical standards, and mentoring experienced practitioners.\nHands-on experience with Databricks or a comparable cloud-based data and analytics platform.\nExceptional written and verbal communication skills, including the ability to explain complex quantitative concepts to non-technical audiences.\nPreferred Qualifications\n\nMaster's degree or PhD in statistics, mathematics, econometrics, operations research, computer science, engineering, or a related quantitative field.\nExperience in a regulated industry such as medical devices, healthcare, pharmaceuticals, life sciences, or financial services.\nDemonstrated depth in one or more areas such as experimentation, causal inference, forecasting, survival analysis, optimization, or advanced machine learning.\nExperience establishing analytical standards, governance practices, reusable frameworks, or enterprise data science capabilities.\nPublications, patents, conference presentations, open-source contributions, or other evidence of external or internal thought leadership.\nExperience advising executive leaders on strategic decisions through quantitative evidence.\nExperience working across geographically distributed, multidisciplinary teams.\nNOTE: This position is eligible for hybrid working arrangements and requires on-site work from an Insulet office.\nAdditional Information:\n\nCompensation & Benefits:\n\nFor U.S.-based positions only, the annual base salary range for this role is $170,600.00 - $255,875.00\n\nThis position may also be eligible for incentive compensation.\n\nWe offer a comprehensive benefits package, including:\n• Medical, dental, and vision insurance\n• 401(k) with company match\n• Paid time off (PTO)\n• And additional employee wellness programs\n\nApplication Details:\nThis job posting will remain open until the position is filled.\nTo apply, please visit the Insulet Careers site and submit your application online.\n\nActual pay depends on skills, experience, and education.Insulet Corporation (NASDAQ: PODD), headquartered in Massachusetts, is an innovative medical device company dedicated to simplifying life for people with diabetes and other conditions through its Omnipod product platform. The Omnipod Insulin Management System provides a unique alternative to traditi...","description_format":"text","description_chars":13229,"description_truncated":true,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":["Continuous learning","Hybrid work","Vision insurance","Wellness"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Diabetes Devices"],"lifecycle":[{"event":"open","at":"2026-10-21T20:37:52Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 37 · green card filings: 17","filings_12m":37,"filings_prev_12m":51,"green_card_filings_12m":17,"median_offered_wage_usd":152778,"route":null,"cap_exempt":false,"checked_at":"2026-10-15T11:24:14+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)","US Department of Labor: PERM disclosure data (green cards)"],"filings_for_role_12m":3}],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":4,"expected_fill_days":28,"reasons":["conf:22","velocity","win:early","comp:brand"],"computed_at":"2026-10-26T05:57:25Z"},"pay":{"stated_usd_annual":255875,"is_top_pay":true},"html_url":"https://alion.io/job/insulet-principal-data-scientist-hybrid","json_url":"https://alion.io/job/insulet-principal-data-scientist-hybrid.json","meta":{"generated_at":"2026-10-26T10:39:41Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":14359,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-27T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":5958},"rest":"https://alion.io/mcp/rest/get_company?id=5958"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Finsulet-principal-data-scientist-hybrid"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Finsulet-principal-data-scientist-hybrid"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Finsulet-principal-data-scientist-hybrid"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/insulet-principal-data-scientist-hybrid\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Finsulet-principal-data-scientist-hybrid"}]}