Sr. Marketing Data Scientist

Founded in 1992 in Dover, NH, Planet Fitness is one of the largest and fastest-growing franchisors and operators of fitness centers in the world by number of members and locations. As of March 31, 2026, Planet Fitness had approximately 21.5 million members and 2,909 clubs across all 50 states, the District of Columbia, Puerto Rico, Canada, Panama, Mexico, Australia, and Spain. The Company’s mission is to enhance people’s lives by providing a high-quality fitness experience in a welcoming, non-intimidating environment, known as the Judgement Free Zone®. Approximately 90% of Planet Fitness clubs are owned and operated by independent business owners.

About the role

Reporting to the Director, Marketing Analytics & Data Science, the Senior Marketing Data Scientist develops advanced analytical solutions, predictive models, and experimentation frameworks that enable Marketing leaders to optimize member growth, marketing effectiveness, and business performance. This role partners with Marketing, Technology, and business stakeholders to apply statistical modeling, machine learning, AI, and other advanced analytics techniques to solve complex business problems. The ideal candidate combines proven predictive and prescriptive modeling expertise, experience building scaled, enterprise-level data science solutions, strong marketing domain business knowledge, and the ability to translate complex insights into actionable recommendations.

This role follows a hybrid schedule and requires regular, in-person work at our Boston, MA or Hampton, NH office. Our hybrid model is Monday, Tuesday, and Wednesday in office; Thursday and Friday are optional work-from-home. Candidates must reside within commuting distance of one of these locations. Fully remote work is not available for this role.

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, 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 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.

Qualifications

• Master’s 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 expertise in developing predictive models and advanced analytical solutions for LTV, segmentation, acquisition propensity, churn, winback, next best action (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; experience with advanced measurement techniques such as Bayesian Structural Time Series is a plus.
• Proficiency in Python, SQL, and modern analytics platforms such as Snowflake, Databricks, or similar cloud-based data environments.
• 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.
• 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, written, and oral communication skills, 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.

Benefits

• Comprehensive benefits package including core medical, dental, vision, life, and disability coverage, as well as supplemental accident, hospital, and critical illness options.
• Generous time off program, including volunteer time.
• Childcare and pet care reimbursement.
• Paid parental leave.
• Tuition reimbursement.
• Free Black Card membership.
• Learning and development programs.
• Engagement activities, team-building, and corporate events.
• 401(k) Plan with safe harbor employer matching.
• Employee stock purchase plan.
• Eligibility to participate in an annual corporate bonus incentive program based on company financial and personal performance.

Pay

The salary for MA-based and NH-based employees hired into this role will be aligned with the range below. This is a good faith estimate, and the amount of base salary will correspond with a candidate’s professional experience, qualifications, and internal equity.

Annual Base Salary Range: $125,000–$150,000

Schedule

Hybrid schedule: Monday, Tuesday, and Wednesday in office (Boston, MA or Hampton, NH); Thursday and Friday optional work-from-home.