Data Scientist - Post Sales

Staff Data Scientist – Post Sales

Location: San Francisco (Hybrid)

Salary: $200–250k base + RSUs

This fast-growing Series E AI SaaS company is redefining how modern engineering teams build and deploy applications. We’re expanding our data science organization to accelerate customer success after the initial sale—driving onboarding, retention, expansion, and long-term revenue growth.

As the senior data scientist supporting post‑sales teams, you will use advanced analytics, experimentation, and predictive modeling to guide strategy across Customer Success, Account Management, and Renewals. Your insights will help leadership forecast expansion, reduce churn, and identify the levers that unlock sustainable net revenue retention.

• Forecast & Model Growth: Build predictive models for renewal likelihood, expansion potential, churn risk, and customer health scoring.
• Optimize the Customer Journey: Analyze onboarding flows, product adoption patterns, and usage signals to improve activation, engagement, and time‑to‑value.
• Experimentation & Causal Analysis: Design and evaluate experiments (A/B tests, uplift modeling) to measure the impact of onboarding programs, success initiatives, and pricing changes on retention and expansion.
• Revenue Insights: Partner with Customer Success and Sales to identify high‑value accounts, cross‑sell opportunities, and early warning signs of churn.
• Cross‑Functional Partnership: Collaborate with Product, Rev Ops, Finance, and Marketing to align post‑sales strategies with company growth goals.
• Data Infrastructure
  
  Collaboration:
  
  Work with Analytics Engineering to define data requirements, maintain data quality, and enable self‑serve dashboards for Success and Finance teams.
• Executive Storytelling: Present clear, actionable recommendations to senior leadership that translate complex analysis into strategic decisions.

• Experience: 6+ years in data science or advanced analytics, with a focus on post‑sales, customer success, or retention analytics in a B2B SaaS environment.
• Technical
  
  Skills:
  
  Expert SQL and proficiency in Python or R for statistical modeling, forecasting, and machine learning.
• Domain Knowledge: Deep understanding of SaaS metrics such as net revenue retention (NRR), gross churn, expansion ARR, and customer health scoring.
• Analytical Rigor: Strong background in experimentation design, causal inference, and predictive modeling to inform customer‑lifecycle strategy.
• Communication: Exceptional ability to translate data into compelling narratives for executives and cross‑functional stakeholders.
• Business Impact: Demonstrated success improving onboarding efficiency, retention rates, or expansion revenue through data‑driven initiatives.