Company Overview

Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With tens of millions of 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.

Job Overview

The Intuit Customer Success (ICS) Data Science & Analytics team is seeking an exceptional Manager 2, Data Science to lead measurement of expert-driven service excellence across our CG expert workforce. You will lead a team of data scientists covering five interlocking areas: expert-driven conversion initiatives, VEP operational capabilities (work item management, scheduling, and appointments), training efficacy and expert upskilling strategy, expert quality and service-guidance adherence, and labor optimization across our Team of Experts. 

Collaborating closely with VEP (Virtual Expert Platform), Expert Network, and Service Design leaders, you will build the measurement backbone needed to see and optimize service excellence end-to-end (E2E) at scale for CG — with a particular focus on conversion and customer-serving time outcomes. 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 is your opportunity to join this exciting and transformational journey.

Responsibilities

• Own the measurement strategy across CG's expert service pillars — expert experiences conversion initiatives, VEP operational capabilities, training and upskilling, expert quality and service-guidance adherence, and labor optimization — in close partnership with VEP, Expert Network, and Service Design leaders.
• Advance causal inference and experimentation as a shared capability, establishing the causal relationship between service guidance, expert behavior, and outcomes like conversion and customer-serving time.
• Contribute to a cross-signal, causal-graph decision engine that connects expert behavior, quality, and operational signals to size the impact of levers and inform roadmap and investment decisions.
• Build and govern the training and learning platform efficacy framework, connecting L&D investment to expert quality and business outcomes.
• Partner with cross-functional business leaders (e.g., VEP, Expert Network, Service Design, Service Delivery, Operations, Learning & Development, Expert Engagement) to shape strategy, investment priorities, and reporting cadence (e.g., WBR, Redzone).
• Lead and develop a world-class team of analysts and data scientists, coaching technical and strategic skill sets, especially in advanced analytics, applied data science, and modeling.
• Translate business and analytics strategies into multiple short-term and long-term projects, and manage end-to-end execution, including the creation of a metric framework for expert experiences and developing learning plans that connect training and service investments to expert experience metrics.
• Activate analytics insights that lead to decisions or better hypotheses, and evangelize innovative analytics ideas that enable new opportunities.
• Drive analytics rigor by inspecting and improving experimentation methodologies and techniques, and creating a learning culture.
• Drive the data roadmap from data quality to data visualization, and construct data models that enable E2E service excellence measurement.
• Role-model "win-together" while challenging the status quo and driving change across teams, regardless of organizational structure.

Qualifications

• 7+ years of diverse analytics and data science experience, including product analytics and customer/expert experience analytics.
• Proven experience leading a team of analysts and/or scientists.
• Strong passion for uncovering strategic opportunities and solving business problems.
• Prior hands-on experience with advanced statistical analysis, controlled experimentation (including A/B testing of hierarchical mixed effects), and applied data science.
• Prior hands-on experience with Causal Inference methods (Propensity Score, DiD, Synthetic Control, etc.) and knowing when to use them to answer key business questions.
• Experience designing quality and experience metrics (e.g., NPS-style scores, quality scorecards) and connecting them to business KPIs such as conversion and service time.
• Proficient with SQL and Python.
• Experience applying AI/ML and GenAI tools to accelerate analysis and workflows.
• Experience with data gap assessments, requirements gathering, and infrastructure implementation.
• Outstanding communication skills with both technical and non-technical colleagues.
• Education: Bachelor's or Master's Degree in a quantitative field (e.g., Statistics, Computer Science, Engineering, Economics, Operations Research, etc.)

Intuit 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:
San Diego $189,000 - $256,000