Mailchimp Customer Success - Staff Data Scientist

Intuit

Quick summary

Work type
On-site
Location
Mountain View, CASan Diego, CA
Salary
$186,500–$252,000 / yr
Posted
57 days ago
Nearby
99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $174k
This role $219k
$123k most similar roles pay here $266k

This role pays more than 85% of similar roles. Most pay $137,250–$211,200 — the shaded band above. At the midpoint, this role pays about $219k versus about $174k 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 124 open roles on FindRole.

Listed pay typically runs $201,500–$273,000 across 111 roles with salary data.

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At a glance

TL;DR · Mailchimp Customer Success - Staff Data Scientist

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As a Staff Data Scientist on the Mailchimp Customer Success Data Science & Analytics team, you will serve as a senior individual contributor, applying deep analytical expertise to complex business problems and partnering closely with leaders across Customer Success, Product, and Operations. Your responsibilities include defining success metrics, designing advanced experiments, developing predictive models, and translating insights into clear recommendations for both technical and non-technical stakeholders. You will work with large datasets, build scalable measurement frameworks, and collaborate with Data Engineering to ensure high data quality and robust analytics assets. The role requires expertise in statistics, causal inference, Python (pandas, numpy, scikit-learn), SQL, and experience with AI/ML-driven customer experiences. Ideal candidates have a background in fintech or SaaS and familiarity with Customer Success metrics and concepts.

Skills

Python pandas numpy scikit-learn statsmodels SQL Advanced SQL A/B testing Causal inference Data Engineering AI/ML LLM-driven customer experiences Tableau Qlik Quicksight git Version control Software engineering best practices

What you'll do

  • Lead the design of rigorous analytical approaches for evaluating Customer Success programs and initiatives.
  • Develop scalable measurement frameworks for key CS outcomes such as engagement, retention, and customer health.
  • Build predictive models to improve targeting and personalization of customer experiences.
  • Translate complex analyses into clear insights and narratives for both technical and non-technical stakeholders.
  • Champion analytics rigor and experimentation best practices across the organization.
  • Partner with Data Engineering to ensure high data quality and scalable analytics assets.

What we're looking for

  • Over 6 years of data science or analytics experience with significant impact in customer success or related domains.
  • Strong expertise in statistics, experimentation, and causal inference methods beyond basic A/B testing.
  • Advanced proficiency in SQL and Python for complex data analysis and modeling (pandas, numpy, scikit-learn).
  • Proven ability to translate large dataset insights into actionable business decisions and strategies.
  • Experience building scalable analytics solutions that enhance efficiency and consistency across teams.
  • Excellent communication skills to influence stakeholders and guide decision-making with clear narratives.

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