Intuit
Senior Staff Data Scientist, Expert Experiences
Intuit, San Diego, California, United States, 92189
Senior Staff Data Scientist, Expert Experiences
Overview
The Intuit Customer Success (ICS) Data Science team is seeking an exceptional and deeply experienced Senior Staff Data Scientist to drive innovation and enhance customer experiences through Intuit's world-class expert services. In this pivotal, high-leverage Individual Contributor role, you will be responsible for defining and building the foundational intelligence system that maps the end-to-end expert journey, identify and quantify points of friction in the expert workflow, establish causal relationships to business outcomes (including Customer Serving Time), and develop reusable analytical frameworks that scale across business units. Collaborating closely with the Intuit Assist Expert Experiences team, your contributions will be instrumental in shaping the future of customer success at Intuit as we build a service platform to empower our customers beyond core product use. This role requires a technical leader who operates as a domain expert, influencing product direction across multiple teams, navigating highly complex problems, and introducing new methodologies to the Data Science community.
Strategic Vision & Framework Development
Strategy to Problem: Demonstrate the ability to turn complex business strategy (e.g., Expert-as-Product, AI-Native Experience) into well-defined, measurable analytical problems and iteratively self-generate and validate hypotheses to create actionable insights.
X-functional Influence: Combine deep insights, business acumen, and strategic considerations to influence cross-functional VPs and Directors on key investment areas and critical priorities across multiple initiatives or business units.
Metric System & Causal Structure: Lead the development and implementation of a tiered metric system that maps the causal relationships between high-level business goals (e.g., efficiency, conversion) and low-level product health metrics (e.g., latency, accuracy, and coverage), leveraging Causal AI methods (like Causal Graphs) to structure and connect input metrics to outcome metrics.
Advanced Causal Measurement & Modeling
Causal Measurement Strategy: Design and implement a durable strategy for measuring the causal impact of expert-facing features, with a primary focus on Customer Serving Time reduction and ensuring GenAI efficiencies fully achieved and accurately attributed. This includes executing complex Randomized Controlled Trials and utilizing advanced methods like hierarchical Bayesian modeling for robust inference.
Model Development & Innovation: Lead the end-to-end development of advanced analytical models, including Behavioral Modeling on clickstream data, Anomaly Detection for friction identification, and Automated Opportunity Sizing models. Apply and drive the use of advanced Causal Inference methods, including Causal Discovery and Causal Graph modeling, to answer the most complex business questions regarding efficiency attribution and metric relationship fidelity.
AI Strategy Co-Creation & Guidance: Co-create the Expert Experiences AI strategy in partnership with cross-functional teams. Guide teams on the phased testing and roll-out of AI-native experiences, ensuring the right processes are in place for causal measurement.
Mentorship & Stewardship
Mentorship: Actively raise the team's technical knowledge, skill, and engagement by mentoring junior employees, documenting and sharing standards, and participating in technical forums.
Data Stewardship: Deeply understand the current state, gaps, and target state of the core expert data layers, providing technical guidance to bridge known data gaps and ensuring data architecture alignment with long-term strategy.
Qualifications
Experience: 7+ years in Data Science, applied Machine Learning, and/or advanced statistical modeling, with a focus on product or customer experience analytics.
Education: Bachelor’s or Master’s Degree in a quantitative field (e.g., Statistics, Computer Science, Engineering, Economics, Operations Research, etc.).
Technical Expertise: Expert proficiency in SQL and Python/R for data analysis, modeling, and pipeline development.
Deep, hands-on experience translating a business problem into a predictive/prescriptive modeling problem and leading the end-to-end model development lifecycle.
Advanced knowledge and applied experience with a wide range of Causal Inference methods (including Causal Graph/Causal AI techniques) and advanced statistical methods.
Experience in AI/ML, Generative AI (GenAI), and LLM integration into analysis/data workflows, including prompt optimization and fine-tuning.
Strategic Impact: Proven track record of influencing Director and VP-level cross-functional partners, driving strategic decisions, and creating reusable analytical assets or frameworks that scale across business units.
Communication: Outstanding communication and data storytelling skills, with the ability to articulate complex technical findings to non-technical executive audiences.
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The Intuit Customer Success (ICS) Data Science team is seeking an exceptional and deeply experienced Senior Staff Data Scientist to drive innovation and enhance customer experiences through Intuit's world-class expert services. In this pivotal, high-leverage Individual Contributor role, you will be responsible for defining and building the foundational intelligence system that maps the end-to-end expert journey, identify and quantify points of friction in the expert workflow, establish causal relationships to business outcomes (including Customer Serving Time), and develop reusable analytical frameworks that scale across business units. Collaborating closely with the Intuit Assist Expert Experiences team, your contributions will be instrumental in shaping the future of customer success at Intuit as we build a service platform to empower our customers beyond core product use. This role requires a technical leader who operates as a domain expert, influencing product direction across multiple teams, navigating highly complex problems, and introducing new methodologies to the Data Science community.
Strategic Vision & Framework Development
Strategy to Problem: Demonstrate the ability to turn complex business strategy (e.g., Expert-as-Product, AI-Native Experience) into well-defined, measurable analytical problems and iteratively self-generate and validate hypotheses to create actionable insights.
X-functional Influence: Combine deep insights, business acumen, and strategic considerations to influence cross-functional VPs and Directors on key investment areas and critical priorities across multiple initiatives or business units.
Metric System & Causal Structure: Lead the development and implementation of a tiered metric system that maps the causal relationships between high-level business goals (e.g., efficiency, conversion) and low-level product health metrics (e.g., latency, accuracy, and coverage), leveraging Causal AI methods (like Causal Graphs) to structure and connect input metrics to outcome metrics.
Advanced Causal Measurement & Modeling
Causal Measurement Strategy: Design and implement a durable strategy for measuring the causal impact of expert-facing features, with a primary focus on Customer Serving Time reduction and ensuring GenAI efficiencies fully achieved and accurately attributed. This includes executing complex Randomized Controlled Trials and utilizing advanced methods like hierarchical Bayesian modeling for robust inference.
Model Development & Innovation: Lead the end-to-end development of advanced analytical models, including Behavioral Modeling on clickstream data, Anomaly Detection for friction identification, and Automated Opportunity Sizing models. Apply and drive the use of advanced Causal Inference methods, including Causal Discovery and Causal Graph modeling, to answer the most complex business questions regarding efficiency attribution and metric relationship fidelity.
AI Strategy Co-Creation & Guidance: Co-create the Expert Experiences AI strategy in partnership with cross-functional teams. Guide teams on the phased testing and roll-out of AI-native experiences, ensuring the right processes are in place for causal measurement.
Mentorship & Stewardship
Mentorship: Actively raise the team's technical knowledge, skill, and engagement by mentoring junior employees, documenting and sharing standards, and participating in technical forums.
Data Stewardship: Deeply understand the current state, gaps, and target state of the core expert data layers, providing technical guidance to bridge known data gaps and ensuring data architecture alignment with long-term strategy.
Qualifications
Experience: 7+ years in Data Science, applied Machine Learning, and/or advanced statistical modeling, with a focus on product or customer experience analytics.
Education: Bachelor’s or Master’s Degree in a quantitative field (e.g., Statistics, Computer Science, Engineering, Economics, Operations Research, etc.).
Technical Expertise: Expert proficiency in SQL and Python/R for data analysis, modeling, and pipeline development.
Deep, hands-on experience translating a business problem into a predictive/prescriptive modeling problem and leading the end-to-end model development lifecycle.
Advanced knowledge and applied experience with a wide range of Causal Inference methods (including Causal Graph/Causal AI techniques) and advanced statistical methods.
Experience in AI/ML, Generative AI (GenAI), and LLM integration into analysis/data workflows, including prompt optimization and fine-tuning.
Strategic Impact: Proven track record of influencing Director and VP-level cross-functional partners, driving strategic decisions, and creating reusable analytical assets or frameworks that scale across business units.
Communication: Outstanding communication and data storytelling skills, with the ability to articulate complex technical findings to non-technical executive audiences.
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