Data Scientist, Marketing & eCommerce

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 production environments.

Pay Range: $100,000- $135,000. Final compensation will be dependent upon skills & experience.

Additional Information

• Thriving Wellness Community: Access to top-notch medical coverage comprehensive vision and dental plans, and a 401k with match eligibility to secure your financial future and including: 
  • 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 
• Career Advancement Opportunities: Unlock your potential with clear paths for career progression and internal mobility across our diverse family of brands and business units. 
• Focused Learning and Development: Grow as a leader with our dedicated Learning and Organizational Development department, offering extensive resources like People Manager and leadership training, webinars, and eLearning courses. 
• Empowering Mentorship Program: Connect with colleagues through our company-wide mentorship program to share goals, overcome challenges, and drive your career forward. 
• Continuous Improvement Training: Participate in training that empowers all employees to learn and implement concepts that drive significant, positive change in the workplace. 
• Building a Healthy Society: To date, our co-Founders Lynda and Stewart Resnick, through their foundations and The Wonderful Company, have invested more than $2.5 billion in education, health and wellness, community development, and sustainability initiatives across California’s Central Valley, Fiji, and the world. In 2025, Wonderful ranked as #1 in PEOPLE’s “100 Companies That Care” list and was named one of Fortune magazine’s 100 Best Companies to Work For. To learn more about our corporate social responsibility efforts, visit csr.wonderful.com.
• Giving Back to the Community: Make a difference with Wonderful Giving, allowing you to allocate company-provided funds to up to three charities of your choice, and join Wonderful Neighbor to contribute to group service projects within our local communities. 

Say everything and share your "Love Out Loud™" with the gift of Teleflora® flowers—all made by hand and delivered by hand by your local florist. With more than 10,000 member florists in North America alone, Teleflora offers the kind of personal touches, artistry and expertise you expect from a trusted neighborhood florist—even if that neighborhood is across the country. No prepackaged flowers in nondescript boxes dropped on your doorstep—Teleflora's network of professional florists creates artistic arrangements personally delivered in a vase, often on the same day. Teleflora makes every day an occasion with a two-in-one gift that includes a multipurpose keepsake container for long-lasting enjoyment. For more, visit: www.teleflora.com, or follow us on Instagram and Facebook and tag your own #LoveOutLoud moment. 

Telefora is part of The Wonderful Company, a successful, fast-growing privately held $6 billion company with 10,000 employees worldwide. The Wonderful Company’s connection to consumers has health at its heart and giving back in its DNA. The company has a long-standing commitment to corporate social responsibility, including more than $1 billion invested in environmental sustainability; $65 million in charitable giving, education initiatives, and innovative health and wellness programs each year; and $143 million toward the construction of two charter school campuses in California’s Central Valley. To view the current Corporate Social Responsibility report, visit csr.wonderful.com.

The Wonderful Company is an Equality Opportunity Employer that provides opportunities for advancement. We are committed to creating a diverse workforce that embodies a deep culture of acceptance, equity, and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, region, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected categories.

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