Interested in this Data Scientist role at Planet Fitness?
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About This Role
About Us:
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 in 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, which we call the Judgement Free Zone®. Approximately 90% of Planet Fitness clubs are owned and operated by independent business owners.
At Planet Fitness, our unique mission has always been to enhance people’s lives by providing a high\-quality fitness experience in a welcoming, non\-intimidating environment. And we’re proud of the amazing Planet Fitness team that supports our clubs and team members. They are comprised of dynamic, dedicated, and talented individuals who represent our values of integrity, transparency, passion, respect, and excellence (while having fun!) in everything they do.
Joining the PF family means being part of a company that cares about bettering the health and wellbeing of our communities. It means being a part of a supportive, engaging workforce with an inclusive culture that values diversity and creates an environment where everyone can feel they belong. It means encouraging professional growth and development. It means making true, lasting connections with your co\-workers with celebrations, team building activities and engaging corporate events! It means creating a positive impact in our local communities through our Judgement Free Generation® philanthropic initiative. It means being part of a brand that you can be proud of!
For the past 30 years, we’ve helped millions of people in their fitness journey and revolutionized the industry along the way. And we’re just getting started!
Overview:
Reporting to the Director, Geospatial Analytics \& Data Science, the Senior Geospatial Data Scientist develops advanced geospatial analytical solutions, predictive models, optimization algorithms, and experimentation frameworks that enable business leaders to optimize market expansion, site selection, member growth, and business performance.
This role partners with Strategy, Real Estate, Finance, Operations, Marketing, and Technology stakeholders to apply geospatial analytics, statistical modeling, machine learning, AI, and optimization techniques to solve complex location\-based business problems.
The ideal candidate combines proven expertise in geospatial data science, predictive modeling, spatial optimization, and enterprise analytics with the ability to translate complex spatial insights into actionable business 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 M/T/W in office and TH/F 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:
Advanced Geospatial Analytics, Data Science \& Measurement* Develops predictive and prescriptive geospatial models supporting market expansion, site selection, trade area analysis, cannibalization, white space identification, member demand forecasting, and location optimization.
- Proposes, designs, and builds scalable, production\-ready geospatial solutions in collaboration with Data Engineering and Technology that become embedded in enterprise real estate and market planning decision processes.
- Applies advanced spatial statistics, machine learning, AI, and optimization techniques to identify market opportunities and improve business performance.
- Evaluates model performance and continuously improves geospatial solutions through agile product development and ongoing validation.
- Develops geospatial forecasting models, scenario analyses, simulations, and optimization models that support strategic planning and investment decisions.
- Designs and evaluates geospatial experiments and measurement methodologies to assess market performance, trade area dynamics, and location strategy effectiveness.
- Translates complex geospatial analyses into actionable recommendations that guide executive decision\-making.
Cross\-Functional Partnership* Works hand in hand with Strategy, Real Estate, and Operations partners to develop the geospatial analytics and data science strategy and roadmap.
- Collaborates with Finance, Marketing, Operations, and Technology stakeholders to solve complex business challenges using advanced spatial analytics.
- Presents analytical findings and recommendations clearly to technical and non\-technical audiences.
- Supports enterprise growth initiatives through advanced geospatial modeling and data\-driven insights.
- Improves geospatial data accessibility, analytical processes, automation capabilities, and model scalability.
- Promotes geospatial analytics, AI, and data science best practices across the organization.
Qualifications:
- Master's degree in Data Science, Geography, GIS, Statistics, Mathematics, Computer Science, Economics, Engineering, Analytics, Operations Research, or a related quantitative field.
- 3\+ years of hands\-on experience developing geospatial data science solutions supporting business strategy, location analytics, or market optimization.
- Demonstrated experience developing predictive models and advanced geospatial analytical solutions for site selection, trade area analysis, market potential, demand forecasting, spatial optimization, customer segmentation, or related location intelligence applications.
- Strong understanding of spatial statistics, machine learning, optimization techniques, predictive modeling, and geospatial experimentation methodologies.
- Experience with GIS platforms such as ArcGIS, Esri, CARTO, Mapbox, QGIS, or similar geospatial technologies.
- Proficiency in Python, SQL, and modern analytics platforms such as Snowflake, Databricks, or similar cloud\-based data environments.
- Experience with geospatial Python libraries such as GeoPandas, Shapely, NetworkX, Rasterio, PySAL, or similar tools is highly desired.
- Experience with MLOps practices and tools such as MLflow, Kubeflow, Docker, or similar technologies to deploy, monitor, and scale machine learning models is a plus.
- Strong problem\-solving skills with the ability to synthesize complex spatial and business datasets into predictive insights and actionable 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!
Perks:
Planet Fitness cares about you and your well\-being. We offer a comprehensive benefits package to eligible employees which includes the core medical, dental, vision, life and disability as well as supplemental accident, hospital and critical illness coverage options. In addition, we are proud to offer eligible employees a generous time off program (including volunteer time), childcare reimbursement, paid parental leave, pet care reimbursement, tuition reimbursement, free Black Card membership, learning and development programs and a whole host of engagement activities. We offer a 401(k) Plan with safe harbor employer matching and an employee stock purchase plan. This role is also eligible to participate in an annual corporate bonus incentive program based on company financial and personal performance.
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 *Note to Applicants:* *We have been made aware of an increasing number of hiring fraud schemes across numerous platforms. Planet Fitness never requires advance payments of any kind for computer equipment or any other purpose at the start of employment. Any request for you to provide payment information during the application process is part of a fraud scheme. Further, we recommend that you do not provide sensitive personal information (SSN, DOB, driver’s license number) as part of the initial application process.*
Salary Context
This $125K-$150K range is below the median for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Planet Fitness, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($137K) sits 29% below the category median. Disclosed range: $125K to $150K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Planet Fitness AI Hiring
Planet Fitness has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Boston, MA, US. Compensation range: $150K - $240K.
Location Context
AI roles in Boston pay a median of $210,000 across 166 tracked positions.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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