Senior Staff Data Scientist, Finance Data Science

Finance Data Science turns complex financial and customer data into trusted decisions, scalable operating processes, and growth opportunities. We partner with GBSG Finance across strategy, embedded FP&A support, external reporting, GBSG LTV, and the foundational customer, subscription, and revenue data that supports the business.

As a Senior Staff Analyst, you will lead the hardest, most ambiguous analytical problems our business faces. You will work closely with GBSG FP&A, Finance Strategy, Product, Engineering, Data Platform, Accounting, and Investor Relations. The role combines financial acumen, data science, business judgment, and executive communication. You will frame the question before analysis begins, establish the metrics and causal levers that matter, and drive the work through to decisions that shape strategy, planning, reporting, and operating processes. You will also help build the data products, evaluation systems, and AI-enabled workflows that make high-quality analysis more reliable and scalable across Finance.

Responsibilities

• Frame and lead cross-team analytical problems involving growth, retention, ARPC, attach, LTV, pricing, portfolio decisions, forecasting, close and reconciliation, and external reporting.

• Define the metrics, business logic, and causal levers that leaders use to manage GBSG performance.

• Own the shared business context, definitions, and analytical reasoning that Finance agents and self-service tools rely on.

• Partner with Finance and business leaders to translate complex data into recommendations, investment choices, operating changes, and executive narratives.

• Build and own shared data products, semantic models, pipelines, and decision systems that multiple teams depend on. Partner with Engineering and Data Platform to harden them for long-term use.

• Establish quality standards, monitoring, governance, and lifecycle ownership for the data products and models that support financial and customer decisions.

• Apply causal inference, experiments, quasi-experiments, and synthetic tests to resolve contested attribution questions and guide decisions where no clean answer exists.

• Turn high-value analytical bottlenecks into scalable AI and agentic workflows by defining quality standards, building evaluation frameworks, and setting appropriate delegation guardrails.

• Shape how Finance uses AI by sizing opportunities against business outcomes, defining what good looks like, and proving which analytical work can be delegated safely.

• Build the evaluation, monitoring, and governance practices that allow teams to use AI-enabled analytical systems with confidence.

• Raise the analytical bar across the team through technical leadership, coaching, reusable methods, and clear standards for decision-quality analysis.

Qualifications

Qualifications

• 10+ years of relevant experience in data science, analytics, finance, strategy, or a related field, with a track record of influencing important business decisions.

• Strong business judgment and financial acumen, with the ability to connect customer, subscription, revenue, and financial data to business outcomes.

• Advanced SQL and programming skills in Python, R, or a comparable language, along with experience working with large-scale data platforms.

• Deep understanding of statistical reasoning, causal inference, experimentation, predictive modeling, and the limitations of each method.

• Experience leading ambiguous, cross-functional problems where the question, metric definitions, or appropriate methodology must be established before analysis can begin.

• Experience designing and owning data products, analytical models, pipelines, or decision systems through their lifecycle.

• Experience evaluating AI-enabled or non-deterministic systems using approaches such as golden datasets, structured testing, synthetic data, monitoring, or human and automated evaluation.

• Strong written and verbal communication skills, including the ability to author executive-level narratives and influence leaders across Finance and partner organizations.

• Ability to operate independently, set direction, and build trust across technical, financial, and business teams.

• Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering, Finance, or a related quantitative field, or equivalent practical experience.

Preferred additional experience

• Experience with SaaS, subscription, or customer lifecycle businesses.

• Experience with LTV, retention, ARPC, attach, pricing, portfolio strategy, forecasting, close, reconciliation, or external reporting.

• Experience serving as a business owner for customer, subscription, revenue, or financial data foundations.

• Experience building AI-enabled analytical workflows, agent context, evaluation harnesses, or delegation governance.

• Experience partnering with Engineering or Data Platform teams to turn analytical prototypes into durable, monitored production data products.

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.