Senior Data Scientist - Customer Success

Senior Data Scientist - Customer Success

Location: San Francisco (Hybrid)

Salary: $200-250k base + RSUs

Join a fast-growing Series E AI SaaS company that is transforming the way modern engineering teams build and deploy applications! We're expanding our data science team to enhance customer success significantly after the sale, focusing on efficient onboarding, retention, expansion, and nurturing long-term revenue growth.

As a key member of our post-sales data science team, you will leverage advanced analytics, experimentation, and predictive modeling to shape strategies across Customer Success, Account Management, and Renewals. Your insights will empower leadership to forecast expansion effectively, mitigate churn, and pinpoint the factors that drive sustainable net revenue retention.

• Forecast & Model Growth: Develop predictive models for renewal likelihood, expansion potential, churn risk, and customer health scoring.
• Optimize the Customer Journey: Investigate onboarding processes, product adoption trends, and usage signals to boost activation, engagement, and time-to-value.
• Experimentation & Causal Analysis: Create and assess experiments (A/B tests, uplift modeling) to quantify the impacts of onboarding programs, success initiatives, and pricing strategies on retention and expansion.
• Revenue Insights: Collaborate with Customer Success and Sales teams to detect high-value accounts, cross-sell advantages, and early indicators of churn.
• Cross-Functional Partnership: Work closely with Product, Rev Ops, Finance, and Marketing to synchronize post-sales strategies with overall company growth targets.
• Data Infrastructure
  
  Collaboration:
  
  Partner with Analytics Engineering to establish data needs, uphold data quality, and create self-service dashboards for the Success and Finance teams.
• Executive Storytelling: Deliver clear, actionable insights to senior leadership with the ability to translate complex analyses into strategic decisions.

• Experience: 6+ years in data science or advanced analytics, focusing on post-sales, customer success, or retention analytics within a B2B SaaS context.
• Technical
  
  Skills:
  
  Proficient in SQL and either Python or R for statistical modeling, forecasting, and machine learning applications.
• Domain Knowledge: Comprehensive understanding of SaaS metrics like net revenue retention (NRR), gross churn, expansion ARR, and customer health scoring.
• Analytical Rigor: Strong foundation in experimental design, causal analysis, and predictive modeling to guide customer lifecycle strategies.
• Communication: Excellent capability to transform data into engaging narratives for executives and cross-functional partners.
• Business Impact: Proven track record of enhancing onboarding effectiveness, retention rates, or expansion revenue through data-driven initiatives.