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Overview

Intuit is seeking an experienced Talent Acquisition Analytics Lead (Senior Staff Data Scientist) to join our People Data & Analytics team. Our team partners closely with Intuit's HR leaders, COEs, and business partners to deliver data-driven insights that shape our people strategies and elevate decision-making across the company. We are looking for a strong Senior Staff Data Scientist to lead Talent Acquisition analytics strategy.

As Talent Acquisition analytics has moved past descriptive reporting, this lead position will drive overall TA analytics strategy focusing on hiring forecasting, talent funnel conversion modeling, and defining and delivering AI-native use cases and prototypes across data & analytics. This role plays a key part in supporting major HR initiatives, including workforce planning and organizational effectiveness. This individual will build trust in TA's data, both in Avature and in the data lake, and translate that trust into a strategic understanding of the talent data ecosystem that strengthens TA's ability to meet business hiring needs and deliver on operational excellence.

This is not purely a technical role. It requires the business acumen and leadership presence to connect insights across the full candidate and employee lifecycle - bringing hiring, attrition, and internal mobility together - and to operate as a trusted advisor to TA leadership, shaping the questions as well as answering them. It also means bringing an external lens into TA: benchmarking Intuit's hiring funnel, forecasting approach, and recruiter productivity against industry standards, so TA's methods keep pace with where the broader market is headed. Working across the broader People Analytics team, you will partner on Talent Acquisition data products, analytics, and models combining hands-on analysis with the advisory judgment to turn findings into decisions.

Responsibilities

Data & Analytics Roadmap Design & Execution

• Translate TA and business strategy into analytical problems, self-generating hypotheses from the data and validating or disproving them to produce recommendations that inform hiring and workforce decisions across multiple initiatives.
• Develop, maintain, and socialize the Talent Acquisition Data & Analytics roadmap, reflecting a prioritized inventory of the current and upcoming work, and drive the business case for the investments it implies.
• Define the metrics that hold TA and business leaders accountable for hiring strength and requisition governance, at the function level rather than the individual report level.
• Combine insights, business acumen, and strategic considerations to influence Director-level and above stakeholders, translating complex analysis into a narrative leaders will act on rather than simply report against.
• Connect insights across the candidate and employee lifecycle, bringing hiring, attrition, and internal mobility together into a single view.
• Bring external analytics benchmarking and TA industry best practices into Intuit, so forecasting, funnel, and recruiter-productivity work is informed by where the broader market and competitors are heading, not only by internal history.

Strategy and Measurement for AI-Native Experiences

• Ideate and scope AI use cases within Talent Acquisition by connecting industry-wide developments in AI with strategic insight, customer knowledge, and TA domain knowledge, from ideation through measurement.
• Influence cross-functional partners in creating a development plan for those use cases, and define success metrics that connect model performance to hiring and business outcomes.
• Provide thought partnership on phased testing and rollout of AI-native experiences, with the right measurement in place at each stage.
• Drive requirements for scaled solutions including automated insights, dashboards, and reporting that support TA leadership, recruiters, HR, and Finance.
• Own TA's data governance framework across Avature and the data lake, ensuring data quality, cleanliness, and readiness for AI and advanced analytics.

Inference and Algorithms

• Build and evolve hiring forecast and funnel conversion models, applying predictive and causal inference techniques to identify trends, friction points, and opportunities to improve hiring outcomes.
• Manage the end-to-end aspects of complex analytics model builds, from scope and requirements through stakeholder management and delivery, for work that drives function-level decisions.
• Partner on recruiter capacity and productivity analytics, bringing recommendations that sharpen capacity planning and target-setting accuracy, and designing new models where the current approach leaves gaps.
• Identify new methodologies and external trends and adapt them to TA use cases, creating shareable frameworks that clarify when and how a new approach should be used.
• Collaborate across TA, People Analytics Research Team, Finance, and other stakeholders to share knowledge and build scalable, enterprise-ready solutions.

Qualifications

• 8+ years of experience in People Analytics, including data science roles, with 5+ years supporting Talent Acquisition analytics.
• Proven experience partnering closely with Talent Acquisition leadership to maintain an end-to-end analytics roadmap, and leading cross-functional analytics initiatives involving multiple stakeholders across HR, technology, and business.
• Demonstrated expertise in building and interpreting hiring models, including predictive and forecast models, and applied experience with causal inference methods such as propensity score, difference-in-differences, or synthetic control, with clarity on when each applies.
• Advanced SQL and fluency in Python or R, with genuine hands-on comfort with data wrangling and analysis.
• Experience scoping and measuring AI-native experiences, with a clear approach to measurement at each stage of phased testing and rollout, and fluency in how model performance metrics connect to business outcomes.
• Proven ability to partner with senior leaders as an advisor, with the judgment to determine how much analysis a decision requires and the credibility to stand behind a recommendation.
• The judgment to connect hiring, attrition, and internal mobility signals into a single lifecycle view rather than treating TA analytics as a siloed function, and to know when a question calls for a deeper model versus a fast, directional read that unblocks a leadership decision.
• Experience working with Avature or other related ATS systems.
• Demonstrated ownership of data governance frameworks and data quality standards, including making an imperfect data source trustworthy and keeping it that way.
• Passion for enabling data-driven, equitable people processes that foster growth and inclusion.

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:
Mountain View $210,500 - $284,500