Staff Data Scientist

ABOUT WAY
 
Way is building the AI, data, and infrastructure layer for the experience economy.

As artificial intelligence automates labor and people reclaim time, the shift toward spending on experiences over things only accelerates. Way sits at the center of that shift as the platform, intelligence layer, and curated marketplace on which experiences are created, distributed, and sold.

Founded in 2020 and headquartered in Austin with offices in Paris and San Francisco, Way built the category of experiences in hospitality from the ground up, during a pandemic that had shut the industry down. Leading global travel, retail, lifestyle, and hospitality brands leverage Way, including Hyatt, Hilton, Alaska Airlines, Aspen, Piaget, and countless others.

Way’s position is layered by design: a proprietary data network built on millions of experiential data points, the system of record for experiential operations, an e-commerce layer embedded directly into partners’ customer journeys, and a marketplace through Partner Exchange, where brands across hospitality, travel, lifestyle, and retail co-create and co-sell experiences with one another. Every brand that joins makes the network more valuable to every brand already on it.

That combination of data, workflow, distribution, and network effects is not something that can be vibe-coded into existence. It compounds. It is what positions Way as the durable infrastructure for the experience economy regardless of how the technology landscape evolves.

Way is backed with $22m in capital from Tiger Global and strategic family offices and angel investors. The company has mid-8 figures of ARR from a global enterprise customer set, with outstanding growth and efficiency metrics.

The Role
We’re looking for a Staff Data Scientist to help build the intelligence layer that will shape the future of Way. You’ll turn the data generated across our platform into trusted insights, models, and decision systems that make every part of the product smarter — from pricing and merchandising to personalization, forecasting, recommendations, and automation. This is a hands-on, staff-level individual contributor role with broad ownership across data science and analytics engineering. You’ll work closely with Product and Engineering to build our data foundation, identify the highest-impact problems to solve, develop and validate solutions, and bring successful work into production. You won’t inherit a mature data science organization, a perfectly clean dataset, or a queue of predefined modeling tasks. We’re looking for someone who is excited to build from the ground up, operate with ambiguity, and play a foundational role in how Way uses data and intelligence across the company and product.

What You’ll Do
Play a foundational role in the future of Way by building the data and intelligence layer that will ultimately power smarter decisions across every aspect of our product - from pricing and merchandising to personalization, forecasting, and automation. Build and own Way’s data and analytics foundation, bringing together product, transactional, customer, and partner data into reliable, reusable data products. Establish the core metrics, data models, and definitions that give Way and our customers a consistent, trustworthy understanding of their business. Identify high-impact opportunities to apply data science and machine learning across the Way platform, then build and ship models, experiments, and decision systems that create measurable value. Partner closely with product and engineering to turn data and models into real product capabilities - progressing from insights, to recommendations, to intelligent automation as the evidence supports it. Set a high bar for analytical rigor and responsible use of data, clearly communicating what the evidence supports, where uncertainty exists, and where better data is needed.

What We’re Looking For
Significant experience applying data science to real product, marketplace, commerce, or customer problems, with a record of shipping models, experiments, or decision systems that drove measurable business impact. Experience building a data and analytics foundation from the ground up - from raw operational and behavioral data through pipelines, modeling, business definitions, and production-ready datasets. Expert SQL and strong Python skills, with strong foundations in statistics, experimentation, forecasting, and model evaluation. Experience working at a high-growth startup or similarly fast-moving environment, where you’ve operated with ambiguity, moved quickly, and taken ownership beyond a narrowly defined role. Strong product judgment: you can take an ambiguous business problem, turn it into a testable question, and choose the simplest rigorous approach capable of answering it. High ownership and clear communication, with the ability to work closely with product and engineering from exploration through production.

Why Way
Way is a company for people who want to do the best work of their careers, at a technology company, but in an industry that will continue to flourish as humanity embraces AI. The standard for building an exceptional software company has changed, and Way is being built to meet it, with urgency, ownership, and a pace of execution well above the industry norm. The market direction is not in question. As AI absorbs more and more work, humans have more time and more money to spend on experiences, and the brands they spend it with need infrastructure they cannot build themselves. Compensation: competitive salary calibrated to experience, and meaningful equity in a sustainably growing company with real value creation already realized. Coverage: full health, dental, and vision. The work: direct access to the world’s most recognized consumer brands and the mandate to move quickly on their behalf. The tools: the best available, and the expectation that you use them to raise your own ceiling. Experience firsthand: an annual hotel and travel stipend at the properties and partners in Way’s global ecosystem. Where you’ll be: in a beautiful office in Austin, San Francisco, or Paris.