Staff Data Scientist

Intuit

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Mountain View, CANew York, NY
Salary
$194,000–$262,500 / yr
Posted
3 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $182k
This role $228k
$114k $278k
below market most similar roles pay here above market

This role pays more than 80% of similar roles. Most pay $144,988–$220,000 — the blue band above. At the midpoint, this role pays about $228k versus about $182k for comparable roles.

Based on 240 similar postings.

Employer

About Intuit

Intuit is a financial software company known for products like TurboTax, QuickBooks, Mint, and Credit Karma, helping consumers and small businesses manage their finances and taxes. Industry: Financial Software & Technology

Intuit currently has 255 open roles on FindRole.

Listed pay typically runs $202,500–$274,000 across 236 roles with salary data.

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View all roles at Intuit

At a glance

TL;DR · Staff Data Scientist

The Staff Data Scientist joins the Product Data Science team to support the Messaging area, focusing on omnichannel customer engagement through SMS, WhatsApp, and RCS. As a senior individual contributor and strategic thought partner, you will tackle complex business problems across the messaging customer journey, including adoption, monetization, and retention. You will lead strategic research, develop scalable data products, and build measurement frameworks to influence product roadmaps. Key responsibilities include performing advanced analytics, causal inference, and predictive modeling to identify growth opportunities. You will utilize SQL, Python, and machine learning libraries like pandas, NumPy, scikit-learn, and statsmodels. Additionally, you will leverage modern AI tools and LLM-based coding assistants to accelerate workflows and provide technical leadership across the broader ecosystem.

What does a Data Scientist earn in California?

Median $214500 from 117 postings across 27 companies.

See salary data

What you'll do

  • Identify and prioritize high-impact growth opportunities across the messaging customer journey to drive revenue.
  • Lead complex analytical investigations using SQL, Python, machine learning, and causal inference methods.
  • Design and evaluate A/B tests and quasi-experiments to quantify incremental product impact.
  • Develop scalable measurement frameworks and success metrics to evaluate messaging product performance.
  • Build reusable analytical frameworks, automated insights, and self-service data products to democratize access to data.
  • Apply predictive modeling and customer segmentation to optimize messaging adoption and customer lifetime value.
  • Partner with engineering teams to improve data instrumentation, quality, and reliability across the ecosystem.
  • Translate complex analytical findings into actionable narratives for senior leadership and non-technical stakeholders.

What we're looking for

  • 8+ years of experience in Data Science, Product Analytics, or a related quantitative discipline.
  • Bachelor's degree in Statistics, Mathematics, Economics, Computer Science, Data Science, Engineering, or a related quantitative field.
  • Advanced degree (preferred).
  • Deep expertise in advanced analytics, experimentation, and causal inference, including A/B testing and quasi-experimental methods.
  • Strong foundation in predictive modeling and machine learning, including customer segmentation and churn prediction.
  • Advanced proficiency in SQL and Python.
  • Experience building scalable data products and analytical frameworks.
  • Working fluency with modern AI tools and workflows.
  • Experience in messaging, marketing automation, communications platforms, or omnichannel customer engagement (preferred).
  • Experience with SaaS monetization, usage-based pricing, product-led growth, and customer lifecycle optimization (preferred).
  • Demonstrated technical leadership through mentoring and scaling data science capabilities (preferred).

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