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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 Vice President, Enterprise Analytics, the Director, Geospatial Analytics \& Data Science serves as the primary analytics partner responsible for proposing, designing, and building enterprise\-wide geospatial and location intelligence solutions and insights that optimize domestic and international market growth and existing and new club performance.
Leading a team of Geospatial Analysts and Data Scientists, this role delivers descriptive, diagnostic, predictive, and prescriptive geospatial analytics solutions that enable leaders to make informed, data\-driven decisions regarding new market expansion, site selection, market planning, M\&A target footprint diagnostics, and network optimization. Working cross\-functionally with Real Estate, Strategy, Development, Marketing, Finance, and Operations, the Director develops scalable, productized geospatial solutions embedded as fundamental elements of overall business strategy and enterprise\-wide decision making.
The Director plays a critical role in advancing Planet Fitness' analytics maturity by replacing fragmented analyses with standardized geospatial analytics solutions built with modern data science methodologies.
The ideal candidate combines strong business acumen, expertise in geospatial analytics and data science, passion for building, deploying, and supporting productized analytics solutions, stakeholder management skills, and a proven track record of partnering with senior leaders to drive measurable business impact.
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:
Functional Analytics Leadership* Serves as the primary geospatial analytics and data science partner to Real Estate, Development, Strategy, Operations, and executive leadership.
- Develops and executes the geospatial analytics roadmap aligned with enterprise growth priorities and business objectives.
- Leads the development of scalable analytics products, predictive models, and location intelligence solutions supporting site selection, market planning, and portfolio optimization.
- Identifies emerging market trends and champions innovative analytics methodologies that improve business performance and strategic decision\-making.
Geospatial Analytics, Data Science, Measurement, \& Reporting* Proposes, designs, develops, scales, and supports predictive models and geospatial analyses supporting site selection, trade area optimization, market prioritization, white space analysis, cannibalization, and portfolio planning.
- Applies GIS, statistical modeling, forecasting, and data science methodologies to evaluate market opportunities and optimize growth strategies.
- Continuously improves geospatial analytics capabilities through enhanced reporting, predictive models, AI\-enabled tools, and location intelligence technologies.
- Translates demographic, competitive, member, operational, and financial data into actionable insights that improve market expansion and business performance.
- Establishes enterprise KPIs, executive dashboards, scorecards, mapping solutions, and self\-service reporting capabilities supporting market planning and portfolio performance.
- Standardizes geospatial methodologies, data definitions, and reporting processes while replacing manual analyses with scalable enterprise analytics products.
- Promotes adoption of geospatial analytics tools and reporting capabilities across business stakeholders.
Cross\-Functional Business Partnership* Collaborates with Strategy, Real Estate, Development, Finance, Marketing, and Operations to align market planning and business priorities.
- Supports enterprise initiatives through advanced analytics, business case development, financial modeling, scenario analysis, and performance measurement.
- Presents analytical findings and strategic recommendations to senior leadership in a clear, concise, and actionable manner.
Team Leadership \& Development* Leads, coaches, and develops a team of Geospatial Analysts and Data Scientists, fostering technical excellence, innovation, and business partnership.
- Establishes priorities, provides guidance on analytical methodologies, predictive modeling, GIS technologies, and executive communication.
- Partners with Enterprise Analytics leadership to advance analytics capabilities, processes, and organizational standards.
Qualifications:
- Masters degree in Geography, GIS, Analytics, Data Science, Statistics, Economics, Computer Science, Mathematics, Urban Planning, Engineering, or a related quantitative field
- 8\+ years of progressive experience in geospatial analytics, GIS, location intelligence, business analytics, data science, market planning, or related analytical disciplines
- 5\+ years of experience leading analytics teams while partnering with senior business leaders to influence strategic decision\-making
- Demonstrated success building scalable geospatial analytics products, predictive models, and decision\-support capabilities that drive measurable business outcomes
- Experience applying geospatial analytics to site selection, trade area analysis, market planning, territory optimization, white space analysis, portfolio optimization, or competitive intelligence
- Strong understanding of statistical analysis, predictive modeling, machine learning, forecasting, and spatial analytics methodologies
- Experience developing executive dashboards, scorecards, interactive mapping solutions, KPI frameworks, and self\-service reporting capabilities
- Expertise with GIS and analytics technologies such as Esri ArcGIS, Alteryx, SQL, Python, Snowflake, Power BI, Tableau, or similar platforms
- Ability to synthesize large, complex geographic and business datasets into clear, actionable insights that support strategic decision\-making
- Extremely detail\-oriented, efficient, and organized with an exceptional ability to establish priorities and objectives and manage multiple projects simultaneously
- Exceptional presentation and communication skills along with the ability to communicate effectively across all levels of the organization, including executives
- Able to establish and maintain effective, collaborative work relationships with diverse individuals, internally and externally
- Creative, progressive, thought leadership with the ability to influence at all levels of the organization
- Excellent leadership and people management skills including the ability to build, motivate, guide, and mentor teams to achieve ambitious goals
- Possess a results\-driven entrepreneurial mindset, demonstrating initiative and creativity
- Demonstrated strategic thinking, creative problem\-solving, and organizational leadership skills
- Exhibits comfort, ease, and flexibility working in an extremely fast\-paced ever\-changing, deadline\-driven environment
- 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: $200,000\-$240,000 *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 $200K-$240K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Planet Fitness, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. Disclosed range: $200K to $240K.
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 AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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