Data Scientist, Marketing & eCommerce

The Wonderful Company•Houston, TX

17h•$100,000 - $135,000•Remote

About The Position

Teleflora is seeking a hands-on Data Scientist to build predictive Customer Lifetime Value (LTV/CLV) models and develop statistical and machine learning models to forecast eCommerce performance. The role works with customer transaction and behavioral data in an eCommerce, direct-to-consumer, subscription, or digital consumer business. The Data Scientist owns the development, validation, and ongoing improvement of predictive LTV models; forecasts revenue; analyzes customer purchasing behavior; and identifies opportunities to improve customer acquisition, retention, and profitability. The role partners with Marketing, Finance, Product, Analytics, Data Engineering, and Data Warehouse teams to support business decisions through advanced analytics. The position is eligible for remote work. Responsibilities include designing, building, validating, and maintaining LTV models; predicting customer value, purchase frequency, repeat purchases, and retention; conducting cohort analysis and segmentation; and applying LTV predictions to acquisition, retention, and marketing investment decisions. The role also develops and maintains forecasts for eCommerce revenue, orders, conversion rates, average order value, and demand; analyzes trends, seasonality, marketing performance, and customer behavior; and builds scenarios to inform growth, risk, budgeting, forecasting, and strategic planning. The Data Scientist analyzes large datasets, applies statistical and machine learning methods, and uses Python, SQL, and cloud-based data platforms to develop and optimize predictive models. The role collaborates to support data quality, scalable data models, and analytical workflows; communicates model findings, assumptions, and recommendations to business stakeholders; and contributes to analytical best practices and continuous improvement. The position also explores AI and Large Language Model technologies and potential applications to analytics, reporting, and business workflows, including participating in the development and testing of AI-powered analytics solutions. Teleflora is part of The Wonderful Company. The posting describes company-wide wellness, learning and development, mentorship, career advancement, community giving, and corporate social responsibility programs. The salary 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 support for 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.

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

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.
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, predictive performance, and forecast accuracy.
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, 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.
Analyze large datasets to identify trends, patterns, and opportunities across eCommerce operations.
Apply 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, accessibility, scalable data models, and analytical workflows.
Explore opportunities to use AI and Large Language Model technologies to improve analytics, reporting, and business workflows.
Collaborate with internal teams to identify potential AI use cases and evaluate their business value; participate in developing and testing AI-powered analytics solutions.
Communicate model findings, assumptions, and recommendations to business stakeholders.
Contribute to analytical best practices and continuous improvement initiatives.

Benefits

Medical coverage, vision plans, and dental plans.
401(k) with match eligibility.
24/7 online physician consultations.
Virtual mental health resources and life coaching.
Employee community groups.
Cash rewards for healthy habits and fitness reimbursements.
Library of on-demand fitness videos.
Career progression and internal mobility opportunities.
Learning and development resources, including people manager and leadership training, webinars, and eLearning courses.
Company-wide mentorship program.
Continuous improvement training.
Wonderful Giving funds that employees can allocate to up to three charities of their choice.
Opportunities to participate in group community service projects through Wonderful Neighbor.