Data Scientist ? Customer Success Analytics

Job Description

We are seeking an experienced Data Scientist ? Customer Success Analytics to lead the design and development of predictive and prescriptive models that power the Customer Success organization. This role will focus on churn and upsell prediction, behavioral signal detection, and proactive intelligence that drives engagement and retention within the account base. The ideal candidate will blend data science expertise, business acumen, and automation mindset to turn large-scale Customer Success and product usage data into actionable insights. Key Responsibilities Predictive & Prescriptive Analytics Develop and maintain churn prediction, upsell propensity, and engagement forecasting models using advanced statistical and machine learning techniques. Create signal intelligence frameworks to identify early indicators of customer risk and opportunity. Apply feature engineering, segmentation, and cohort analysis to improve predictive accuracy. Automation & Scalability Automate recurring analytics tasks, reports, and alert systems using Python, SQL, and workflow orchestration tools (Airflow, Power Automate). Partner with Data Engineers to operationalize ML models and integrate outputs with CRM and analytics dashboards. Proactive Intelligence & Benchmarking Design baseline and benchmarking frameworks across industries, revenue tiers, and client types. Generate client vs. industry and client vs. revenue-range comparative insights to support CSM strategies. Deliver proactive signals, trend detection, and alerts for retention and expansion opportunities. Data Quality & Governance Define and enforce data validation rules, ensuring sensible data checks and anomaly detection. Evaluate data completeness and correctness; work with Data Engineering to resolve pipeline or data quality issues. Cross-Functional Collaboration Partner with Customer Success, Product, and Sales teams to embed data-driven recommendations into playbooks and engagement models. Collaborate with BI and MIS teams to ensure model outputs are visible, actionable, and measurable in dashboards. Qualifications Required Bachelor?s/Master?s degree in Data Science, Statistics, Computer Science, Applied Mathematics, or related field. 8 ?10 years of experience in applied data science, preferably in SaaS or B2B environments. Hands-on expertise in Python (pandas, scikit-learn, NumPy, matplotlib, seaborn) and SQL. Experience in predictive modeling, clustering, classification, and regression techniques. Proficiency with BI and visualization tools (Power BI, Tableau) for integrating and communicating model outputs. Familiarity with CRM data (HubSpot, Salesforce, Gainsight) and usage analytics. Exposure to cloud data environments (AWS, Azure, GCP) and ML pipeline deployment preferred. Strong communication skills with ability to translate complex models into actionable business insights. Key Competencies Predictive & Statistical Modeling Machine Learning Implementation Signal & Trend Intelligence Design Data Quality & Governance Cross-Functional Collaboration Automation & Scalable Model Deployment Storytelling with Data Customer Success Metrics Expertise (NRR, GRR, Churn, Adoption, NPS) Proactive, Problem-Solving Mindset Outcome Ownership Move Customer Success from reactive insights to predictive intelligence. Deliver automated signal systems that surface churn or upsell opportunities before CSM intervention. Maintain <2% model error deviation across renewal and adoption predictions. Ensure continuous learning loop between CRM, BI, and data science models for higher forecasting accuracy.