Data Scientist, Marketing & eCommerce

Teleflora • Minneapolis, CA

2d • Remote

About The Position

Teleflora is seeking a hands-on Data Scientist to build predictive Customer Lifetime Value (LTV) models and develop statistical and machine learning models for eCommerce forecasting. The role focuses on customer transaction and behavioral data in an eCommerce, direct-to-consumer, subscription, or digital consumer business. The Data Scientist will own the development, validation, and ongoing improvement of LTV models; forecast revenue; analyze purchasing behavior; and identify opportunities to improve customer acquisition, retention, and profitability. The position partners with Marketing, Finance, Product, Analytics, Data Engineering, and Data Warehouse teams to support business decisions through advanced analytics. It is eligible for remote work and also offers opportunities to explore AI and Large Language Model technologies as their use expands across analytics and business workflows.

Responsibilities span predictive modeling, eCommerce forecasting, advanced analytics and data modeling, AI innovation, and cross-functional collaboration. The role develops and maintains models for customer value, purchasing frequency, repeat purchases, retention, revenue, orders, conversion rates, average order value, and customer demand. It also analyzes customer and business trends, develops scenario models, supports planning, and communicates findings and recommendations to stakeholders.

Teleflora is part of The Wonderful Company. The company describes its benefits and programs as including medical, vision, and dental coverage; a 401(k) with match eligibility; virtual physician and mental health resources; life coaching; employee community groups; healthy-habit rewards and fitness reimbursements; career development, learning, mentorship, and continuous-improvement training; and opportunities to direct company-provided charitable funds and participate in community service projects. The stated pay range is $100,000–$135,000, with final compensation dependent on skills and experience.

Requirements

• At least 3 years of hands-on data science and predictive modeling experience, including experience supporting an eCommerce, direct-to-consumer, subscription, or digital consumer business.
• Proven experience independently designing, building, and validating predictive LTV/CLV models that estimate future customer value from 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 for 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.

Nice To Haves

• Experience with Google Cloud Platform (GCP) and BigQuery.
• Familiarity with AI, Large Language Models (LLMs), or generative AI applications, and interest in developing AI-powered analytics solutions.
• Experience deploying predictive models into production environments.

Responsibilities

• Design, build, validate, maintain, and continuously improve predictive Customer Lifetime Value (LTV/CLV) models using customer transaction and behavioral data.
• Develop statistical and machine learning models to predict customer value, purchasing frequency, repeat purchases, and retention.
• Estimate customer value over time and identify factors that influence long-term profitability.
• Conduct cohort analysis and customer segmentation to identify trends in purchasing behavior and customer value.
• Apply LTV predictions to customer acquisition, retention, and marketing investment decisions in partnership with Marketing and Product teams.
• Develop and maintain models forecasting eCommerce revenue, orders, conversion rates, average order value, and customer demand.
• Analyze historical trends, seasonality, marketing performance, and customer behavior to identify drivers of business performance.
• Build scenario models to evaluate growth opportunities, business risks, and potential outcomes.
• Partner with Finance and Marketing on budgeting, forecasting, and strategic planning.
• Evaluate and improve forecast accuracy and model performance.
• Analyze large datasets to identify trends, patterns, and opportunities across eCommerce operations.
• Use statistical analysis and machine learning to understand customer behavior, purchasing patterns, conversion drivers, and marketing effectiveness.
• Use Python, SQL, and cloud-based data platforms to build, test, and optimize predictive models.
• Collaborate with Data Engineering and Analytics teams to support data quality, consistency, and accessibility, and to develop scalable data models and analytical workflows.
• Explore AI and LLM applications for analytics, reporting, and business workflows; identify use cases, assess business value, and participate in developing and testing AI-powered analytics solutions.
• Work with Marketing, Finance, Product, Analytics, Data Engineering, and Data Warehouse teams to develop data-driven solutions and support analytical initiatives.
• Communicate model findings, assumptions, and recommendations to business stakeholders.
• Contribute to analytical best practices and continuous improvement initiatives.

Benefits

• Medical coverage, comprehensive vision and dental plans, and 401(k) with match eligibility.
• 24/7 online physician consultations and virtual mental health resources.
• Life coaching, employee community groups, cash rewards for healthy habits, fitness reimbursements, and on-demand fitness videos.
• Career advancement opportunities, including career progression and internal mobility across brands and business units.
• Learning and development resources, including leadership training, webinars, and eLearning courses.
• Company-wide mentorship program.
• Continuous improvement training.
• Company-provided funds that employees can allocate to up to three charities of their choice, and opportunities to participate in local group service projects.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level