AirDNA started in a warm California garage in 2015 with an ambitious vision. Since then, the company has grown into the leading provider of data and business intelligence for the billion‑dollar travel and vacation rental sector, with offices located in Denver and Barcelona.

Our self-serve MarketMinder platform removes uncertainty and gives Airbnb hosts the smart, competitive insights they need to thrive in the fast-changing short‑term rental market.

We also support enterprise clients with tailored reports and detailed dashboards that help them scale and invest wisely. Our customers include hundreds of major financial institutions, real estate firms, vacation rental managers, and destination marketing organizations worldwide.

We monitor the daily performance of more than 14 million Airbnb, Vrbo, and Booking.com properties across 120,000 global markets, along with data from thousands of partner properties. By combining scraped and direct data and enhancing it with proprietary algorithms, we deliver the most accurate and comprehensive solutions available.

We believe data is not the finish line but the starting point — the foundation for any future-focused business.

The AirDNA Team:

At AirDNA, we’re a group of data enthusiasts dedicated to helping our customers grow their businesses intelligently. We’re looking for a team leader who can apply an agile mindset to build advanced, scalable acquisition strategies. In short, things are never dull here. We truly live our values of being Happy, Hungry, and Honest, and we especially encourage applicants who embody these principles. Even if you’re unsure whether you meet every requirement, we welcome your application. We thoughtfully consider every candidate, not only those who match all criteria.

The Role:

As a Senior Data Scientist developing software for short‑term rental hosts, you will play a central role in using data-driven insights to design, build, and maintain tools that help hosts invest wisely, manage properties efficiently, streamline operations, optimize pricing, and deliver outstanding guest experiences. At AirDNA, models are core to everything we build. A Senior Data Scientist helps set and execute strategies to continuously enhance our algorithms in a rapidly evolving industry. You will work closely with product managers, engineers, and designers to create intuitive and impactful tools that empower hosts and maximize their success.

What you’ll do:
• Shape the strategy for creating or improving ML systems that help hosts make informed decisions, including revenue estimation, pricing optimization, inventory management, demand forecasting, guest profiling, and sentiment analysis.
• Apply advanced analytics, statistical modeling, and machine learning to extract meaningful insights from complex datasets such as host performance, guest behavior, and market trends.
• Collaborate with product managers, engineers, and designers to define requirements, set priorities, and deliver effective tools for investors and hosts.
• Build predictive models, algorithms, and optimization methods to support data-driven decisions on comp sets, pricing, occupancy, availability, and revenue.
• Lead the integration and deployment of data science models within the software stack, ensuring they are scalable, reliable, and high-performing.
• Stay current on new developments in data science, machine learning, and software engineering, and apply them to enhance our tools.
• Develop strategies to gather and use short‑term rental training data at scale.
• Create processes to continuously evaluate and improve model performance.

What you’ll need:
• A Bachelor’s, Master’s, or PhD in Data Science, Computer Science, Statistics, or a related quantitative field.
• At least 5 years of professional experience as a Data Scientist, with a focus on software development and advanced analytics.
• A proven history of designing, building, and launching successful software solutions driven by data science.
• Experience in short‑term rental investment, revenue management, price optimization, time‑series modeling, or forecasting is a strong advantage.
• Strong programming skills in Python and familiarity with libraries such as Pandas, Scikit‑learn, XGBoost, PyTorch, or TensorFlow.
• Proficiency in SQL and database systems.
• Experience working with large datasets in distributed environments like PySpark and Snowflake.
• Background in ML engineering, cloud deployment, and managing the full model lifecycle.