Hybrid Machine Learning Engineer Machine Learning Engineer with verification

We're revolutionizing the fitness & wellness industry, and we’re looking for talented people to help us do it. Mindbody + ClassPass bring together the best of both sides of the market: Mindbody is the industry’s most trusted all-in-one technology platform; ClassPass is one of the most popular apps for fitness & self-care enthusiasts. Together we’re partnering with more than 70,000 fitness studios, gyms, salons, and spas around the world. We’re not just another tech company—we’re far and away the leader of our industry. So join the team, work with mission-led people, and enjoy amazing benefits. Let’s see what we can accomplish together!Who we areWe’re a passionate engineering, platform, and operations team, developing and supporting ground-breaking products. Together, we’ll build for the future, creating more opportunities for wellness businesses around the world to help their customers lead full, healthy lives. We push ourselves and our company to always strive for this higher purpose, recognizing the power in working together toward the same goal. We believe in building a diverse company where everyone feels safe bringing their authentic selves to work. And we believe that the secret to success is our people. Join the team, and let’s see what we can accomplish together! You'll likely spend time working on

• Engineer predictive models for a wide range of B2B and B2C applications
• Deploy and monitor predictive models into production
• Engineer and blend data from different sources to be used for training pipelines predictive pipelines or direct human consumption
• Collaborate with Product Managers to ensure what is built has the desired customer impact
• Create technical design documentation for new features as a collaborative tool with other engineers
• Write and executes tests for your own code
• Perform peer code reviews to ensure quality of the codebase
• Keep abreast of new technologies

You'll thrive in this role with experience inSuccessful candidates will be strong individual contributors, possess intense curiosity about how data, predictive modeling and AI can be used to create solutions for complex business problems in the B2B and B2C context.Preferred Qualifications:

• 2+ years of experience in building machine learning models and shipping to production
• Advanced degree in a quantitative field (Economics, Physics, Finance, Mathematics, Engineer, etc.)
• Proficient at SQL and Python
• Superb communication skills in both speech and in writing
• Deep sense of intellectual curiosity
• Excited by results and not methods
• Drive to be part of the solution and curious enough to find it
• Ability to consider higher-level business context and thinking while still approaching analytical problems with technical precision and data-mindedness

Have we piqued your curiosity?Sound like the role for you? We’d love to hear from you! Even if you’re not 100% sure about potential fit, we still encourage you to apply. We’re looking for the right person, not the perfect series of checkboxes.Mindbody is an Equal Opportunity Employer. We highly value diversity at our company and encourage people of all different backgrounds, experiences, abilities and perspectives to apply. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or other protected characteristics.By entering your email and phone number and submitting your application, you consent to receive emails, calls and SMS about your application and other roles at Mindbody, including by auto-dialer. Message and data rates may apply. Opt-out or text STOP to cancel at any time. If you are a California resident or reside outside the United States then by submitting your application you confirm that you have read, understood, agree and - where applicable - grant your prior, free, informed and express consent for the processing of your personal information, including sensitive personal information, as described in our