Intuit Staff Data Scientist 

United States, California, San Diego 

249452480

Join the Intuit Customer Success team as a Staff Data Scientist within our Expert Network. In this role, you will help shape the AI strategy for our product support and live experiences. You’ll be central to optimizing our greatest resource—our people—through innovating, experimenting, learning, pivoting, and scaling AI-driven solutions.

• Perform hands-on data analysis and modeling with large datasets.
• Lead experimental designs and measurement plans.
• Apply data mining, NLP, and machine learning (both supervised and unsupervised) to improve relevance and personalization algorithms.
• Build optimization models and simulation tools to support strategic decisions across customer success operations, including workforce planning, customer escalations, root cause analysis, scheduling, and routing.
• Develop and operationalize decision science frameworks to optimize support costs and maximize customer outcomes.
• Partner with stakeholders across Customer Success, Operations, and Strategy teams to embed AI/ML solutions in real-time decision engines and operational workflows.
• Work side-by-side with product managers, software engineers, and designers in designing models, experiments, and minimum viable products.
• Develop generative AI capabilities to power our Expert Network and streamline processes, uncover insights, and reduce the manual burden on the team.

• BS or MS degree in Statistics, Mathematics, Operations Research, Computer Science, Econometrics, or a related field.
• Advanced proficiency in SQL and a statistical programming language such as Python and/or R.
• 8+ years of experience in data mining algorithms and statistical modeling techniques such as clustering, classification, regression, decision trees, neural nets, support vector machines, anomaly detection, recommender systems, sequential pattern discovery, and text mining.
• Deep understanding of Generative AI and other evolving technologies to accelerate insights from multi-modal data.
• Expertise in experimental design and Multivariate/A-B testing.
• Strong background in optimization techniques, such as linear programming, integer programming, constraint optimization, and simulation modeling.
• Experience building and deploying operational models for staffing, queue management, or dynamic resource allocation.
• Familiarity with customer success metrics and operational KPIs such as Net Promoter Score (NPS), Average Handle Time (AHT), First Contact Resolution (FCR), and agent utilization.
• Ability to formulate data-backed strategies that will drive step-function growth for the business as well as increase customer benefit.
• Solid communication skills: Demonstrated ability to explain complex technical issues to both technical and non-technical audiences