Job description
Description
What You’ll Do
Analyze & Uncover Insights
Conduct deep-dive analyses on retention, repeat rate, churn, cohorts, LTV, and lifecycle KPIs
Analyze customer behavior across the funnel (first purchase → repeat → loyal customer)
Perform exploratory data analysis (EDA) to answer ad-hoc retention and lifecycle questions
Identify drivers of churn and loyalty, delivering root-cause analysis with actionable recommendations
Model & Predict
Build and maintain cohort-based and predictive models (churn prediction, LTV forecasting)
Design and analyze A/B tests and lifecycle experiments (email, SMS, loyalty, offers, timing)
Support incrementality measurement for retention initiatives and CRM campaigns
Apply regression and time-series models to forecast retention and repeat revenue trends
Automate & Scale
Create Python/SQL scripts to automate recurring retention and cohort reporting
Build scalable data pipelines for customer lifecycle metrics
Develop QA processes to ensure accuracy and consistency in customer-level data
Partner with the Business
Translate retention and lifecycle business questions into technical analyses
Present insights clearly to non-technical stakeholders (CRM, Marketing, Growth, Finance)
Collaborate with BI, Growth, and Lifecycle teams to define metrics and dashboards
Proactively surface insights that improve customer experience and long-term value
Share Knowledge
Document retention methodologies, analyses, and models
Mentor junior analysts on lifecycle analytics best practices
Help establish analytical standards for retention and customer insights
Why Trafilea
We’re a tech-led eCommerce group scaling our own globally loved DTC brands, while helping ambitious talent grow just as fast.
🚀 We build and scale our own brands.
🦾 We invest in AI and automation like few others in eCom.
📈 We test fast, grow fast, and help you do the same.
🤝 Be part of a dynamic, diverse, and talented global team.
🌍 100% Remote, USD competitive salary, paid time off, and more.
Requirements
Technical Skills (Must Have)
Advanced SQL: Complex queries, CTEs, window functions; experience with data warehouses (BigQuery, Snowflake, Redshift)
Python for Analytics: Pandas, NumPy; visualization libraries; automation scripting
Statistical Modeling: Regression, cohort analysis, time-series forecasting, hypothesis testing
Retention & Lifecycle Analytics: Strong understanding of churn, retention curves, cohorts, LTV, repeat purchase behavior
Data Visualization: Ability to build clear dashboards and charts; experience with BI tools (Looker, Tableau, Power BI, etc.)
Technical Skills (Nice to Have)
Experience with CRM or lifecycle channels (Email, SMS, Push, Loyalty programs)
A/B testing and experimentation frameworks for retention initiatives
Incrementality measurement for lifecycle campaigns
E-commerce or DTC analytics background
Soft Skills
Analytical mindset: Structured, hypothesis-driven problem solver
Communication: Translate customer data into clear business narratives
Business acumen: Strong understanding of unit economics and LTV impact
Proactivity: Identifies retention opportunities without being asked
Collaboration: Works cross-functionally with Growth, CRM, Product, and BI
Attention to detail: High standards for data quality, documentation, and reproducibility
