Customer Success Staff Data Scientist

What you'll do

• Design and execute advanced experimentation and causal inference methods to measure the impact of human-assisted success motions.
• Develop scalable measurement frameworks for key outcomes such as customer engagement, retention, and share-of-wallet.
• Build predictive models and durable segmentation approaches to improve targeting and prioritization across the services portfolio.
• Translate complex data analyses into clear, actionable narratives and roadmaps for senior leadership and cross-functional partners.
• Use AI tools and LLM-assisted development environments to accelerate analytics workflows while ensuring high accuracy.
• Evaluate AI/ML-powered customer experiences by building measurement frameworks for agentic and human-in-the-loop motions.
• Partner with Data Engineering to ensure high data quality and scalable assets during platform migrations.

What we're looking for

• 8+ years of experience in data science, analytics, or product analytics with impact in customer success, product, or go-to-market domains.
• Deep expertise in advanced analytics including causal inference, quasi-experimental design, statistical modeling, and complex experimentation.
• Experience building predictive models and segmentation approaches for production decisions such as propensity, churn, LTV, and customer health.
• Advanced proficiency in SQL and Python for analysis, modeling, and experimentation using libraries like pandas, numpy, scikit-learn, and statsmodels.
• Working fluency with modern AI tooling and LLM-based coding assistants to accelerate data science workflows.
• Proven ability to manage large, complex datasets and translate insights into actionable business decisions for senior stakeholders.
• Excellent communication and storytelling skills to influence cross-functional partners and leadership.
• Bachelor's degree in a quantitative field; advanced degree (preferred).