Senior Data Scientist, Developer Efficiency

OverviewCompany OverviewIntuit is the global financial technology platform that powers prosperity for the people and communities we serve. With approximately 100 million customers worldwide using products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible.About PDX (Platform And Development Xceleration Group)Platform and Development Xceleration Group (PDX) is the architectural and technology foundation of Intuit. PDX delivers an X-multiplier impact across the Tech Ecosystem by accelerating how teams build, operate, and scale products.PDX’s purpose is to provide product development teams and customers with delightful experiences and foundational capabilities that accelerate innovation, engineering excellence, and business outcomes. Our North Star is to create an adaptive, intelligent ecosystem powering frictionless, personalized, end-to-end product development experiences with 10x impact.PDX operates with a customer-back model that emphasizes clear ownership, end-to-end accountability, and ruthless prioritization to focus resources on the highest-impact initiatives.Job OverviewThe Development Efficiency Data Science team within PDX builds the analytical foundations that quantify how effectively Intuit converts developer time into delivered value.We are seeking a Senior Data Scientist to drive hands-on modeling, analysis, and interpretation of Development Efficiency. This role is focused on deep analytical execution, not reporting or dashboarding, and operates in highly ambiguous, cross-cutting problem spaces.The Senior Data Scientist will work closely with the Senior Manager and Data Engineering partners to build robust models, surface insights, and ensure Development Efficiency metrics withstand scrutiny from senior leadership and Finance.ResponsibilitiesModeling and AnalysisDesign, implement, and maintain statistical models used to measure Development Efficiency and its driversBuild and maintain:Baselines and counterfactualsCohort and time-series analysesDecomposition and attribution models across Inner and Outer Loop activitiesQuantify and communicate bias, variance, and uncertainty in efficiency measurementsInsight Generation and CommunicationEvaluate claims of efficiency gains from tooling, process, or behavioral changes using rigorous analytical methodsTranslate complex analyses into clear narratives and visualizations tailored to senior, non-technical stakeholdersProactively surface risks, anomalies, and misinterpretations in reported metricsCollaboration and ExecutionPartner with Data Engineering to ensure analytical data pipelines are reliable, reproducible, and fit for modelingContribute to documentation of assumptions, methodologies, and known limitationsSupport the Senior Manager, engineering and other key stakeholders in evolving metric definitions and analytical standardsQualifications5+ years of hands-on experience in data science, applied statistics, or advanced analyticsStrong expertise in statistical modeling, causal inference, and time-series analysisExperience working with incomplete, noisy, or behaviorally influenced dataProficiency in Python, SQL, and common data science toolingStrong analytical reasoning and data storytelling skillsBachelors Degree in quantitative field required, advanced degree preferredIntuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. The expected base pay range for this position is:Bay Area, California $159,500 - $215,500