Sr. Marketing Data Scientist

Planet Fitness•Boston, MA

21h•$125,000 - $150,000•Hybrid

About The Position

Planet Fitness is seeking a Senior Marketing Data Scientist to develop advanced analytical solutions, predictive models, and experimentation frameworks to optimize member growth, marketing effectiveness, and business performance. This role involves partnering with Marketing, Technology, and business stakeholders to apply statistical modeling, machine learning, and AI to solve complex business problems. The ideal candidate will possess strong predictive and prescriptive modeling expertise, experience building scalable data science solutions, marketing domain knowledge, and the ability to translate complex insights into actionable recommendations. This is a hybrid role requiring regular in-person work at our Boston, MA or Hampton, NH office, with M/T/W in-office and TH/F optional work-from-home. Candidates must reside within commuting distance of these locations.

Requirements

Masters degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Analytics, Engineering, or a related quantitative field
3+ years of hands-on experience deploying data science for marketing solutions
Demonstrated experience and expertise in developing predictive models and advanced analytical solutions for LTV, segmentation, acquisition propensity, churn, winback, NBA, and related marketing and CRM solutions
Strong understanding of experimentation methodologies including A/B testing, incrementality testing, causal inference, attribution, geo-lift analysis, and statistical hypothesis testing
Proficiency in Python, SQL, and modern analytics platforms such as Snowflake, Databricks, or similar cloud-based data environments.
Strong problem-solving skills with the ability to synthesize complex datasets into predictive insights and actionable business recommendations.
Extremely detail-oriented, efficient, and organized with an exceptional ability to establish priorities and objectives
Excellent presentation and written and oral communication skills along with the ability to communicate effectively across all levels of the organization
Able to establish and maintain effective, collaborative work relationships with diverse individuals, internally and externally
Dedicated learner with a natural curiosity for consistent growth
Cooperative team player with an upbeat, positive, “can-do” attitude!

Nice To Haves

experience with more advanced measurement techniques such as Bayesian Structural Time Series is a plus
Experience with MLOps practices and tools such as MLflow, Kubeflow, or Docker to deploy, monitor, and scale machine learning models in production is a plus

Responsibilities

Develops predictive and prescriptive models and solutions supporting member acquisition, retention, lifetime value, segmentation, next best action, and marketing optimization.
Proposes, designs, and builds scaled, productionalized solutions in collaboration with Data Engineering and Technology which are embedded as a core part of standard marketing business decision processes.
Translates complex analytical outputs into actionable recommendations for Marketing and business leaders.
Evaluates model and solution performance and continuously improves them through time by deploying agile product development and management techniques.
Designs and analyzes experiments including A/B testing, incrementality testing, and other measurement methodologies.
Develops frameworks to evaluate marketing effectiveness, customer behavior, and business outcomes.
Supports forecasting, scenario analysis, and optimization efforts through advanced analytics.
Works hand in hand with Marketing counterparts to develop a marketing data science and analytics strategy and roadmap.
Collaborates with Finance, Operations, and Strategy stakeholders to understand business challenges and develop analytical solutions.
Presents analytical findings and recommendations clearly to technical and non-technical audiences.
Supports strategic initiatives through advanced analytics and data-driven insights.
Improves data accessibility, analytical processes, and automation capabilities.
Promotes data science best practices and responsible use of advanced analytics.