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Come join the Intuit Customer Success (ICS) Data Science team as a Staff Data Scientist . This role will be pivotal in shaping how we measure, grow, and optimize the externalization of Intuit's expert capabilities across ICS and various business segments . You will Design, build, and ship GenAI solutions from prototype to production., align on learning plans, improve product functioning, reduce customer friction, and guide externalization strategy in close partnership with the business owners and platform team.
The ideal candidate thrives at the intersection of data science, LLM engineering and unstructured data mining, collaborating closely with engineering, and business teams to drive measurable impact on customer experience. This is a high-impact role where your work will directly influence customer experience, and Intuit's expert platform strategy.
As a Staff Data Scientist, you will operate as a senior individual contributor, partnering closely with peers and leaders across ICS and VEP to identify, validate, and refine innovative strategies for unstructured text data mining. Your expertise - especially in NLP, LLM, defining success metrics, structuring learning plans, and GenAI solution governance- will be instrumental in surfacing critical insights and translating them into actionable hypotheses that fuel sustained product and customer growth.
Responsibilities
Define Success & Drive Product Strategy through structured & unstructured data -- Translate product and business problems into analytical frameworks. Partner with ICS leadership and the VEP product team to define north-star metrics, align on learning plans, and establish what success looks like at each stage of the customer and product journey.
Cross-Functional Influence -- Serve as the strategic data science partner to leaders across ICS, VEP Product, Engineering, Data Engineering, and Business. Translate complex analytical findings into clear, compelling recommendations for executives and stakeholders.
Design, build, and ship GenAI solutions from prototype to production.
Architect Context Engineering pipelines leveraging knowledge graphs.
Lead prompt engineering: system/tool prompts, function calling, prompt versioning with offline/online evals.
Implement evaluation & observability with ground source of truth establishment, confusion metrics, LLM-as-judge with human review, cost & latency monitoring.
Partner with business owners, legal/security to ensure safety, privacy, and measurable business impact.
Insights at Scale -- Conduct deep-dive analyses on unstructured text data and customer insights to inform strategic decisions. Create dashboards, visualizations, and self-serve tools -- including GenAI/LLM-powered applications -- to democratize access to insights across cross-functional teams and leadership.
LLM Infrastructure & Governance -- Partner with data engineering and platform teams to define tracking solution requirements, and ensure reliable, scalable data pipelines and solution instrumentation are in place. Champion data hygiene and integrity.
Qualifications
8+ years of experience in data science, with a proven record of applying advanced analytical methods to drive product or business growth, ideally in SaaS or financial technology companies serving consumer or B2B segments
Proven experience in unstructured text analytics & mining, success metric definition, learning plan alignment
Strong business acumen, excellent communication and storytelling skills with a track record of simplifying the complex and delivering compelling narratives to stakeholders at all levels through data-driven insights
Advanced skills in SQL, Python, and other analytical tools, with practical experience using data visualization platforms (e.g., Tableau, Qlik) to communicate insights; experience with data integration and pipeline development is a plus; familiarity with LLMs and GenAI workflows to build intelligent visualizations and analytical tools is strongly preferred
demonstrated proficiency in causal inference techniques, statistical modeling, machine learning, and experimental design
Experience using statistics and machine learning techniques to solve complex business problems, e.g., product funnel optimization, propensity for feature adoption, next best action models, and friction point identification.
Proficiency in Python and experience with LangChain/LlamaIndex (or equivalent).
Experience with cloud LLM providers (Azure, OpenAI, AWS Bedrock, Vertex AI) and orchestration (Airflow/Dagster).
Security/privacy mindset (PII handling, RBAC), and practical cost/performance tuning.
Deep understanding of retrieval strategies, prompt patterns, model context management, and hallucination mitigation.
B.S. or Ph.D. in a quantitative field (e.g., Statistics, Computer Science, Economics, Mathematics, Operations Research) or equivalent work experience
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will 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 ). 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 $194,000 - $262,500