Senior Data Scientist
Role details
Job location
Tech stack
Job description
We are seeking a Senior Data Scientist to join the Intuit Customer Success (ICS) Data Science & Analytics team supporting Expert Experiences and Intuit Academy. This role focuses on delivering high-quality, decision-ready analytics and data science models that inform training effectiveness, expert readiness, and customer success outcomes.
You will collaborate closely with cross-functional partners across Product, Learning & Development, Quality, Operations, and Analytics to translate ambiguous business questions into clear analytical problems, design reusable analytical frameworks, and develop scalable insights and recommendations. The ideal candidate brings strong analytical judgment, rigor, and the ability to operate independently in complex problem spaces.
Collaborating closely with the Intuit Assist Expert Experiences team, your contributions will be instrumental in shaping the future of customer success at Intuit as we build a service platform to empower our customers beyond core product use.
Responsibilities
Data Science & Analytical Problem Solving
- Translate ambiguous business questions into well-defined analytical hypotheses, causal frameworks, and measurable outcomes.
- Own the end-to-end data science lifecycle, including data acquisition, exploration, validation, feature engineering, statistical modeling, causal inference, and results interpretation.
- Apply advanced statistical and machine learning techniques to uncover drivers of performance, behavior, and outcomes.
- Ensure analytical outputs are statistically sound, reproducible, and decision-ready.
Experimentation, Causal Inference & Measurement
- Design, implement, and analyze experiments (e.g., A/B tests, quasi-experiments, pilots) to estimate the causal impact of interventions.
- Apply causal inference methods (e.g., matching, regression, difference-in-differences) to address confounding, bias, and incomplete data.
- Establish measurement frameworks that balance rigor with practical constraints in real-world data environments.
Data Interpretation & Insight Communication
- Synthesize complex analytical results into clear, actionable insights that influence strategy and product or program decisions.
- Communicate findings through compelling data narratives and visualizations tailored to technical and non-technical stakeholders.
- Collaborate cross-functionally to align on assumptions, metrics definitions, and interpretation of results.
Scalable & Reproducible Analytics
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Develop standardized metrics, analytical frameworks, and reusable data science assets.
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Contribute to scalable dashboards and reporting pipelines that support ongoing measurement and experimentation.
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Partner with Data Engineering and Analytics teams to ensure data reliability, consistency, and analytical best practices.
Requirements
- 4+ years of experience in data science or analytics roles, preferably in product, web, customer care, or customer experience analytics.
- Strong proficiency in SQL and experience working with large-scale data platforms (e.g., Spark, Databricks, BigQuery, Redshift).
- Experience using BI and visualization tools (e.g., Tableau, Qlik, Dash) to deliver clear, stakeholder-ready insights.
- Solid experience with experimentation and causal analysis (e.g., A/B/n testing, applied causal methods), with good 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.
- Strong business acumen , with the ability to translate business questions into testable hypotheses and actionable insights.
- Excellent data storytelling and communication skills , with the ability to influence decisions across technical and non-technical audiences.
- Comfortable working in a fast-paced, ambiguous environment , with flexibility to shift priorities and collaborate across cross-functional teams.
