Manager , Workforce Solutions & Bill Products - Data Science

Overview

Intuit’s Global Business Solutions Group (GBSG) is dedicated to powering prosperity for small and mid-sized businesses (SMBs) through data-driven innovation that simplifies how they move and manage their money. The Payments Data Science & Analytics team develops insights, models, and scalable analytics solutions that fuel growth, efficiency, and world-class customer experiences across Intuit’s Workforce Solutions and Bill Pay ecosystems. The Group Manager, Data Science will lead a team of senior data scientists and people managers to define the strategy, vision, and roadmap for Payments data science, championing the use of AI and advanced analytics to identify growth opportunities, optimize risk and revenue, and enhance customer trust while fostering a high-performance, customer-obsessed culture.

• Lead and scale a high-performing team of data scientists, analysts, and data science managers to deliver end-to-end insights and modeling solutions that accelerate Workforce & Bill Pay growth, efficiency, and customer satisfaction.
• Develop and execute the strategic vision for Workforce & Bill Pay data science, aligning data science priorities with GBSG’s business strategy and Intuit’s broader AI-driven expert platform goals.
• Partner with Workforce & Bill Pay product, engineering, and marketing leaders to deliver insights that improve customer acquisition, onboarding, retention, and lifetime value — balancing growth with risk management.
• Drive predictive and generative AI innovation, including applications for customer segmentation, transaction forecasting, fraud mitigation, and personalized experiences.
• Own and evolve measurement frameworks for key business and customer outcomes, including growth, profitability, risk, and customer experience metrics.
• Champion experimentation and data-driven decision-making, using hypothesis-driven analysis to uncover opportunities for step-change improvements in Workforce & Bill Pay conversion, authorization rates, and customer satisfaction.
• Serve as a thought partner to Workforce & Bill Pay and GBSG executives, influencing strategic decisions through compelling storytelling and actionable data narratives.
• Foster a culture of inclusion, innovation, and continuous improvement, empowering teams to deliver measurable business impact and grow their careers within a dynamic, fast-paced environment.
• Collaborate cross-functionally across Intuit’s ecosystem (Product, Customer Success, Risk, Finance, Marketing, and Design) to deliver shared success metrics and advance enterprise-wide data maturity.

• 12+ years of experience in data science, analytics, or related quantitative disciplines; 6+ years of people leadership experience, including leading other managers and senior ICs.
• Proven ability to define and execute data science strategies that deliver measurable business growth, revenue impact, and customer benefit.
• Deep experience with payments, fintech, or financial services, including familiarity with customer lifecycle analytics, transaction data, and risk modeling.
• Demonstrated expertise in supervised and unsupervised learning, experimentation design, and advanced statistical modeling.
• Track record of influencing senior executives and partnering across functions to align on shared business goals and KPIs.
• Strong background in data storytelling, translating complex quantitative findings into clear, actionable recommendations.
• Passion for developing talent, building high-performing, diverse teams, and advancing analytics maturity within large organizations.
• Extreme ownership mindset with operational rigor, adaptability, and a focus on business outcomes.

• Proficiency in SQL, Python, R, and data visualization tools (Tableau, Qlik, Looker, or equivalent).
• Expertise in machine learning and predictive analytics, including experience with classification, regression, clustering, and embedding models.
• Strong understanding of data infrastructure and pipelines supporting large-scale data products.
• Familiarity with modern AI/ML techniques (e.g., LLMs, reinforcement learning, generative AI) and their application to customer and operational use cases.
• Expe…