TrafileaData

Sr Data Analyst (Retention Focus)

RemotePosted today

The role involves analyzing customer retention, lifecycle, and behavior data to generate insights, build predictive models, automate reporting, and collaborate with various teams to improve customer experience and business outcomes. It requires strong technical skills in SQL, Python, and data visualization, along with excellent communication and business acumen.

Location: Remote

Responsibilities

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
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
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
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
Document retention methodologies, analyses, and models
Mentor junior analysts on lifecycle analytics best practices
Help establish analytical standards for retention and customer insights

Requirements

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.)