Data Analyst II, Customer Lifecycle

About AgelessRx

AgelessRx is a first-of-its-kind, longevity-focused telehealth platform with an e-commerce component. Our mission is to collectively give people millions of extra healthy years, so everyone can enjoy more of what they love in a world where people are empowered to live as long as they want. We believe aging should no longer be treated as a dreadful inevitability, but instead, as a puzzle that can be solved, a fight that can be fought – just as a disease with a cure. Through our free-to-use platform, we offer trusted, data-driven longevity solutions and scientifically backed prescription therapies to help safely lower the risk of age-related diseases. 

About the role
The Data Analyst II, Customer Lifecycle at AgelessRx will harness the power of data to understand customer behavior, propel business growth, and facilitate informed decision-making across diverse departments. Collaborating closely with the Senior Director of Data Analytics, the Director of Finance, as well as department managers, the Data Analyst will adeptly identify business needs, ascertain requirements, and present practical solutions. 

In this role, the Analyst will proactively suggest enhancements to AgelessRx's existing business processes, drawing inspiration from industry trends, cutting-edge technologies, and proven best practices. By fostering a culture of data-driven decision-making, they will contribute to the organization's continuous improvement and success. 

What you'll do

• Stakeholder Collaboration & Requirement Gathering: 
  Partner with cross-functional teams—Marketing, Product, Finance—to define analytics needs and align with business objectives, providing data-driven insights to support customer lifecycle and retention strategies. 

• Data Collection & Analysis: 
  Use SQL, Excel, and Python to collect, clean, and analyze large datasets from various sources, including transactional, behavioral, and engagement data. Support decision-making with insights tied to KPIs and lifecycle metrics. 

• Data Visualization & Reporting: 
  Design and maintain dashboards and reports in Tableau to track key metrics like retention, LTV, cohort performance, and conversions. Communicate findings clearly to both technical and non-technical audiences. 

• Retention Modeling & Statistical Analysis: 
  Build predictive and statistical models (e.g., churn prediction, LTV, segmentation) to uncover trends and inform retention initiatives. Apply techniques such as logistic regression, clustering, and survival analysis. 

• Customer Behavior & Journey Analysis: 
  Analyze behavioral data and map customer pathways to understand engagement patterns, funnel drop-off, and churn triggers. Identify opportunities to enhance the customer experience and lifecycle performance. 

• Business Process Optimization & Insight Generation: 
  Analyze business processes and data to identify opportunities for optimization and innovation. Provide insights into customer behavior, operational efficiency, and areas for growth, recommending actionable improvements to maximize performance. 

• Python-Based Strategy Development: 
  Leverage Python for lifecycle modeling, segmentation, forecasting, and experimentation. Build reusable code and workflows to support scalable insights and data products. 

• Continuous Improvement: 
  Continuously assess and improve analytical tools, workflows, and reporting. Stay up-to-date on best practices in retention analytics and lifecycle modeling. 

• Cross-Functional Support: 
  Deliver insights that inform lifecycle campaigns, retention efforts, and customer engagement strategies. Partner with teams across the org to share data extracts, define audience segments, and track initiative performance. 

Qualifications

• Education & Experience:
• • Bachelor’s degree in a quantitative field (e.g., Data Science, Statistics, Economics); Master’s a plus.
  • 3–5 years of experience in analytics, ideally focused on customer lifecycle, retention, or e-commerce.
• Technical Skills:
• • Proficient in SQL, Python, and Excel for data analysis and automation.
  • Skilled in building dashboards and visualizations using Tableau or similar tools.
  • Experience working with large, diverse datasets from CRM, web, and transactional sources.
• Analytics & Modeling:
• • Strong grasp of statistical techniques (e.g., regression, clustering, A/B testing).
  • Hands-on experience with predictive modeling for churn, LTV, and segmentation.
• Business Insight & Communication:
• • Understanding of lifecycle metrics and KPIs (e.g., retention, churn).
  • Effective communicator with both technical and non-technical stakeholders.
  • Proven ability to align data insights with business strategy across teams.
• Mindset & Impact:
• • Process-oriented with a proactive, solutions-driven approach.
  • Comfortable in fast-paced, collaborative environments.
  • Passion for continuous improvement and data-driven decision-making.