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Arkansas Staffing

Senior Staff Data Scientist

Arkansas Staffing, San Diego, California, United States, 92189

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Senior Staff Data Scientist

We are seeking a highly accomplished Senior Staff Data Scientist to join our TurboTax Data Science Team, driving end-to-end product innovation and personalization across the conversion funnel through advanced analytics and machine learning. This is a mission-critical role requiring a strategic thought partner who brings cutting-edge data science techniques and deep domain expertise to influence product strategy, customer experience, and business growth at scale. You will partner directly with cross-functional leadersacross product, design, engineering, and marketingto architect the analytical frameworks and modeling solutions that shape the user experience for millions of TurboTax customers. This role demands a strong foundation in statistical methods, machine learning, experimentation design, and causal inference, coupled with a demonstrated ability to lead with influence, navigate ambiguity, and execute with precision. What you'll bring: Master's degree in Computer Science, Statistics, Econometrics, Data Science, or a quantitative field. 10+ years of progressive experience in applied data science roles with increasing scope and complexity. Proven experience applying state-of-the-art machine learning and causal inference methodologies in high-impact, product-facing applications. Expert-level proficiency in SQL as well as Python or R. Demonstrated success integrating ML models into production environments, especially within personalization, recommendation, or AI-assisted UX. Deep knowledge of experimental design, including non-standard A/B testing methods, uplift modeling, and sequential testing frameworks. Hands-on experience with data visualization tools like Tableau or Qlik. Strong communication and storytelling abilitiesadept at translating sophisticated analytics into strategic guidance. Proven leadership in mentoring technical talent and driving cross-team alignment through data science innovation. How you will lead: Advanced Predictive Modeling & Machine Learning: Design, build, and deploy scalable modelsincluding ensemble methods, time-series forecasting, LTV modeling, deep learning architectures, and uplift modelingto uncover high-impact growth opportunities and drive personalization. Experimentation Science & Design: Own the end-to-end experimentation pipelinefrom hypothesis generation and design (e.g., CUPED, multi-armed bandits, Bayesian Inference) to rigorous causal interpretation and impact quantification. Causal Inference: Lead causal inference and econometric analyses to understand and influence key levers of business growth with a crisp understanding of incremental impact. Data-Driven Product Strategy: Embed deeply with TurboTax product teams to proactively identify friction points and shape roadmap priorities using granular funnel diagnostics, behavioral clustering, and AI-powered customer journey optimization. Metric Design & Impact Attribution: Define and evolve success metrics using state-of-the-art measurement frameworks, ensuring that business KPIs are both predictive and causally informative. Communication: Deliver compelling, data-driven narratives to VP and Director stakeholders; distill complex findings into clear, actionable strategy recommendations with quantified business impact. ML Engineering & Native AI: Collaborate with the Central AI team to productionalize models that enhance personalization and automation throughout the TurboTax user experience. Thought Leadership & Mentorship: Mentor senior data scientists and establish best practices in experimental design, model validation, and responsible AI usage; drive a culture of analytical excellence and scientific rigor. Strategic Influence: Demonstrate extreme ownership across cross-functional initiatives, influencing product vision and delivering measurable impact through analytics innovation. EOE AA M/F/Vet/Disability. Intuit will consider for employment qualified applicants with criminal histories in a manner consistent with requirements of local law.