Data Scientist, Marketing & eCommerce

Responsibilities

You will design, build, and maintain predictive Customer Lifetime Value models and develop machine learning models to forecast eCommerce performance. Additionally, you will collaborate with cross-functional teams to provide data-driven insights and explore AI and LLM technologies to optimize business workflows.

Requirements

The role requires at least 3 years of hands-on experience in data science, specifically with predictive modeling in eCommerce or direct-to-consumer environments. Candidates must possess advanced proficiency in Python and SQL, along with a Master's degree in a quantitative field or equivalent experience.

Skills

• Data Science
• Predictive Modeling
• Customer Lifetime Value
• Machine Learning
• Statistical Modeling
• Python
• SQL
• eCommerce Forecasting
• Cohort Analysis
• Customer Segmentation
• Data Analysis
• Google Cloud Platform
• BigQuery
• Large Language Models
• Generative AI

Benefits

• Medical coverage
• Vision plan
• Dental plan
• 401k with match
• Virtual mental health resources
• Life coaching
• Fitness reimbursements
• Mentorship program
• Charitable giving program

About this role

Company Description
Teleflora provides innovative marketing, education, and technology to make sure our member florists get the resources they need to thrive, creating beautiful bouquets with keepsake vases delivered to your door. We’ve been a part of The Wonderful Company family since 1979, and with a nationwide network of florists, we’ve become the world’s largest flower delivery service. With more than 10,000 member florists in North America, we lead the industry by working directly with our florists to hand-arrange and hand deliver every bouquet!
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 will is eligible for remote work.
Job Description
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.
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.
Preferred Qualifications
Experience with Google Cloud Platform (GCP) and BigQuery.
Familiarity with AI, Large Language Models (LLMs), or generative AI applications, with an interest in developing AI-powered analytics solutions.
Experience deploying predictive models into pr...

About The Wonderful Company

Food and Beverage Manufacturing · 1,001-5,000 employees · headquartered in Los Angeles, CA