Data Scientist, Expert

We are seeking a Staff Data Scientist to join the Intuit Customer Success Data Science & Analytics team supporting Expert Experiences and Intuit Academy. This role is focused on setting the analytical bar for high-impact, decision‑critical work that informs training effectiveness, expert readiness, and customer success outcomes.

As a Staff Data Scientist, you will operate as a technical leader and thought partner, shaping how analytics and data science are applied across complex, ambiguous problem spaces. You will collaborate closely with cross‑functional partners across Product, Learning & Development, Quality, Operations, Engineering, and Analytics to translate business questions into robust analytical frameworks, guide methodological decisions, and ensure outputs are truly decision‑ready.

Working closely with the Intuit Assist Expert Experiences ecosystem, your contributions will help shape how we scale expert quality and enablement as Intuit builds a service platform that empowers customers beyond core product use.

Lead the framing of ambiguous, high‑impact business problems into clear analytical hypotheses, causal frameworks, and success metrics.

Set and reinforce standards for analytical rigor, assumption validation, internal review, and “definition of done.”

Serve as a technical role model by producing bar‑setting analyses that others can learn from and calibrate against.

Own complex, end‑to‑end analytical initiatives, including data exploration, validation, feature engineering, statistical modeling, causal inference, and interpretation.

Apply advanced statistical, experimental, and machine learning techniques to understand drivers of expert performance, learning outcomes, and customer experience.

Design and review experimentation strategies (A/B tests, pilots, quasi‑experiments) to ensure methodological soundness and business relevance.

Synthesize complex findings into clear, compelling narratives that influence senior stakeholders and inform product, program, and operational decisions.

Act as a trusted advisor by surfacing tradeoffs, risks, and implications—not just results.

Partner cross‑functionally to align on metrics definitions, assumptions, and interpretation before analysis is shared broadly.

Develop reusable analytical frameworks, standardized metrics, and reference implementations that scale quality across the team.

Guide the development of dashboards, reporting pipelines, and measurement systems in partnership with Data Engineering and Analytics.

Improve analytical maturity across the org by reducing rework, catching issues earlier, and enabling others to deliver higher‑quality outputs.

7+ years of experience in data science or analytics roles, preferably in product, web, customer care, or customer experience analytics.

Advanced proficiency in SQL and experience working with large‑scale data platforms (e.g., Spark, Databricks, Big Query, Redshift).

Strong experience with experimentation and causal inference (e.g., A/B/n testing, applied causal methods), with excellent judgment on when and how to apply them.

Familiarity with AI/ML and GenAI‑enabled analytics, and the ability to reason about implications for measurement, experimentation, and user behavior.

Demonstrated ability to lead through influence, not authority—guiding analytical direction, standards, and decisions across teams.

Strong business acumen and ability to translate strategy into testable hypotheses and actionable insights.

Excellent data storytelling and communication skills, with the ability to influence both technical and non‑technical audiences.

Comfortable operating in fast‑paced, ambiguous environments, balancing rigor with pragmatism.