Data Scientist - Customer Segmentation & LTV Analysis

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We are seeking an experienced Data Scientist to support our customer segmentation and LTV analysis initiatives. You will be responsible for connecting various data sources, conducting hypothesis-driven analyses, and building custom dashboards to visualize insights. The ideal candidate has experience in direct-to-consumer (DTC) e-commerce and is adept at translating data insights into actionable strategies. This is a project-based role with the potential for ongoing freelance work based on project outcomes and business needs. Key Responsibilities: Data Integration & Management: - Connect and integrate data from multiple sources (e.g., Shopify, CRM systems, Google Analytics, ad platforms) - Develop and maintain ETL pipelines for seamless data processing -Ensure data accuracy, consistency, and security Customer Segmentation & LTV Analysis: - Build customer segmentation models based on demographic, behavioral, and psychographic data - Calculate Customer Lifetime Value (LTV) using statistical models - Identify high-value customer cohorts and forecast retention trends Hypothesis-Driven Data Exploration: - Formulate data-driven hypotheses about customer behavior - Perform A/B tests and cohort analyses to validate insights - Present findings with clear recommendations for marketing and product strategies Dashboard Development & Data Visualization: - Create custom dashboards to track segmentation metrics, LTV, and marketing performance - Develop visualization tools for presenting data insights - Customize reporting formats based on stakeholder requirements Performance Tracking & Reporting: - Monitor key performance indicators (KPIs) related to segmentation and customer value - Provide insightful reports to guide data-driven decisions - Share progress updates and final deliverables with project stakeholders **Detailed project description attached as PDF. **

Keyword: Customer Retention

Customer Segmentation Data Visualization Marketing Analytics Data Analysis Data Science Statistics Quantitative Analysis