Ebay CRM Analytics Manager 

India, Karnataka, Bengaluru 

360950248

As Analytics Manager, you will own the strategy and execution of data-driven CRM measurement and optimization. You’ll combine rigorous experimentation with machine-learning and AI to personalize experiences, improve engagement and retention, and increase customer lifetime value (CLV). The portfolio may focus on Buyer, Seller, or a cross-company initiative.

• Achieve tangible results. Establish benchmarks and learning goals; develop structures that link campaign performance to business results, CLV, retention.
• Advance personalization with ML/AI. Develop or partner on models (propensity, churn, next-best action, send-time/content optimization, uplift modeling) and integrate them into CRM decisioning and orchestration.
• Lead experimentation at scale. Build and run A/B and multivariate tests; apply sound statistical methods (power analysis, capping, sequential testing, causal inference) to produce trustworthy results.
• Develop robust analytics. Use SQL/Python for data modeling and reproducible pipelines; compose self-serve datasets and dashboards to accelerate decision-making.
• Optimize channel strategy. Evaluate email, push, in-app, onsite, and lifecycle programs; recommend frequency, targeting, and creative strategies grounded in data.
• Tell the story. Translate complex analysis into clear, actionable recommendations for senior collaborators; influence roadmaps and CRM evolution.
• Improve the craft. Guide analysts, advocate for guidelines in documentation, code review, and experimentation hygiene; promote an inclusive, growth-focused culture.
• Safeguard data use. Partner with Legal/Privacy to ensure compliance (e.g., GDPR), guardrails, and responsible AI practices.

• Hands-on CRM analytics experience delivering impact in a consumer or marketplace context.
• Machine-learning & AI experience applied to CRM/personalization (e.g., propensity, churn, LTV/CLV modeling, recommendation/next-best action, uplift).
• Experimentation & statistics expertise, including test building, sample sizing, variance reduction, and causal inference.
• SQL and Python proficiency for data wrangling, modeling, and automation; familiarity with data warehouses and version control.
• Dashboarding & data visualization skills (Tableau preferred) to build clear, credible self-serve insights.
• Business sense proven through translating insights into strategies that yield measurable outcomes.
• Communication & collaborator leadership across Marketing, Product, and Engineering; ability to influence at executive level.

• Experience with marketing tech stacks (contact strategy decisioning/orchestration, unified data platforms, experimentation platforms) and event-level data.
• Familiarity with Spark/Databricks, Airflow, or similar for scalable pipelines.
• Knowledge of generative AI applications for CRM content/testing and guardrail development.
• Marketplace or two-sided platform experience.