Data Scientist, Marketing & eCommerce

Customer Lifetime Value (LTV) & Predictive Modeling

• Independently design, build, validate, and maintain predictive Customer Lifetime Value (LTV/CLV) models using customer transaction and behavioral data.

• Develop statistical and machine learning models to predict future customer value, purchasing frequency, repeat purchases, and retention.

• Apply predictive modeling techniques to estimate customer value over time and identify factors influencing long-term profitability.

• Perform cohort analysis and customer segmentation to identify trends in purchasing behavior and customer value.

• Evaluate and continuously improve model accuracy, reliability, and predictive performance.

• Partner with Marketing and Product teams to apply LTV predictions to customer acquisition, retention, and marketing investment decisions.

eCommerce Forecasting & Advanced Analytics

• Develop and maintain statistical and machine learning models to forecast eCommerce revenue, orders, conversion rates, average order value (AOV), and customer demand.

• Analyze historical trends, seasonality, marketing performance, and customer behavior to identify key drivers of business performance.

• Build scenario models to evaluate growth opportunities, business risks, and potential outcomes.

• Partner with Finance and Marketing to support budgeting, forecasting, and strategic planning.

• Continuously evaluate and improve forecast accuracy and model performance.

Advanced Analytics & Data Modeling

• Analyze large datasets to identify trends, patterns, and opportunities across eCommerce operations.

• Apply statistical analysis and machine learning techniques to understand customer behavior, purchasing patterns, conversion drivers, and marketing effectiveness.

• Leverage Python, SQL, and cloud-based data platforms to build, test, and optimize predictive models.

• Collaborate with Data Engineering and Analytics teams to ensure data quality, consistency, and accessibility.

• Support the development of scalable data models and analytical workflows.

AI & Analytics Innovation

• Explore opportunities to apply AI and Large Language Model (LLM) technologies to improve analytics, reporting, and business workflows.

• Collaborate with internal teams to identify potential AI use cases and evaluate their business value.

• Develop familiarity with emerging AI tools, technologies, and applications.

• Participate in the development and testing of AI-powered analytics solutions as these capabilities evolve.

Cross-Functional Collaboration

• Partner with Marketing, Finance, Product, and Analytics teams to understand business challenges and develop data-driven solutions.

• Work closely with Data Engineering and Data Warehouse teams to support analytical initiatives.

• Communicate model findings, assumptions, and recommendations to business stakeholders.

• Contribute to analytical best practices and continuous improvement initiatives.