[Hiring] Data Scientist @The Wonderful Company

Role Description

We are seeking a hands-on Data Scientist with proven experience building predictive Customer Lifetime Value (LTV) models and developing statistical and machine learning models to forecast eCommerce performance. The ideal candidate has experience working with customer transaction and behavioral data within an eCommerce, direct-to-consumer (DTC), subscription, or digital consumer business.

In this role, you will own the development, validation, and ongoing improvement of predictive LTV models while forecasting revenue, analyzing customer purchasing behavior, and identifying opportunities to improve customer acquisition, retention, and profitability. You will partner closely with Marketing, Finance, Product, and Data Engineering teams to support business decisions through advanced analytics.

This position also offers the opportunity to explore emerging AI and Large Language Model (LLM) technologies as we expand their use across analytics and business workflows. This position is eligible for remote work.

Qualifications

• 3+ years of hands-on experience in data science and predictive modeling, including experience supporting an eCommerce, direct-to-consumer (DTC), subscription, or digital consumer business.
• Proven experience independently designing, building, and validating predictive Customer Lifetime Value (LTV/CLV) models that estimate future customer value using transaction and behavioral data.
• Strong experience developing statistical and machine learning models for eCommerce revenue forecasting, customer purchasing behavior, and retention.
• Advanced proficiency in Python and SQL, with demonstrated experience developing predictive models using customer-level datasets.
• Experience applying statistical modeling and machine learning techniques to customer behavior, purchase frequency, and retention prediction.
• Strong understanding of model development, validation, performance evaluation, and continuous improvement.
• Experience conducting cohort analysis and customer segmentation to support customer analytics.
• Ability to translate predictive modeling results into actionable recommendations for Marketing, Finance, and Product teams.
• Master's degree in Data Science, Statistics, or a related quantitative field, or equivalent practical experience.

Requirements

• 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.
• 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.
• 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.
• 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.
• 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.

Benefits

• Access to top-notch medical coverage, comprehensive vision and dental plans, and a 401k with match eligibility.
• 24/7 online physician consultations.
• Virtual mental health resources.
• Life coaching.
• Engaging employee community groups.
• Cash rewards for healthy habits and fitness reimbursements.
• Library of on-demand fitness videos.
• Clear paths for career progression and internal mobility across our diverse family of brands and business units.
• Extensive resources for learning and development, including webinars and eLearning courses.
• Company-wide mentorship program to share goals and drive your career forward.
• Training that empowers employees to learn and implement concepts that drive significant, positive change in the workplace.