Senior Staff Data Scientist - Engagement & Retention

Intuit

Oakland (CA)

Vor Ort

USD 211.000 - 285.000

Vollzeit

14 Tage+

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Zusammenfassung

Intuit, the Credit Karma Engagement team, is seeking a Sr. Staff Data Scientist in Oakland to lead analytics across activation, habit formation, retention, churn prevention, and reactivation.

You will drive strategy, influence leadership, and advance causal inference approaches while mentoring teammates and shaping the AI-native roadmap with cross-functional partners. The role emphasizes building value-driven features, cross-team collaboration, and rigorous measurement to improve member

Qualifikationen

  • 9+ years of experience driving strategy and impact across initiatives.
  • Experience in consumer product engagement, retention, or growth preferred.
  • Proven ability to translate business strategy into analytical problems.

Aufgaben

  • Set data science strategy across engagement and lifecycle initiatives.
  • Influence senior leadership and cross-functional partners up to VP level.
  • Advance science with new ML and causal inference methodologies.
  • Lead scalable experimentation and apply causal inference where needed.
  • Build durable segmentation strategies for targeted experiences.
  • Shape analytics/AI roadmap with cross-functional teams.
  • Mentor Data Scientists and raise scientific standards.

Kenntnisse

Data science leadership
Experiment design
Causal inference
Customer segmentation
A/B/n experiments
Communication
Mentoring

Ausbildung

Statistics/Mathematics/OR/CS

Tools

Offline ML models
Causal inference frameworks

Jobbeschreibung

Intuit, the Credit Karma Engagement team, is seeking a Sr. Staff Data Scientist in Oakland to lead analytics across activation, habit formation, retention, churn prevention, and reactivation.

You will drive strategy, influence leadership, and advance causal inference approaches while mentoring teammates and shaping the AI-native roadmap with cross-functional partners. The role emphasizes building value-driven features, cross-team collaboration, and rigorous measurement to improve member

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