Data Scientist

Data Scientist

Revenue Analytics is a SaaS company that helps companies make better revenue decisions in pricing, products, and promotions. Our analytics solutions drive measurable revenue uplift and help customers move faster with more confidence.

Founded in 2005, we combine deep expertise in pricing and revenue management with modern software, machine learning, and AI to power complex commercial decisions. In Hospitality, we are building products that help redefine what modern revenue management looks like — turning advanced analytics into practical product capability for hotel operators navigating pricing, demand, and competitive complexity.

This Data Scientist position represents a unique opportunity to join a growth-stage company that is investing aggressively in the future of hospitality revenue management. We are looking for someone excited by that opportunity and motivated to help build real product capability, not just one-off analysis.

As a Data Scientist, you will help build machine learning and AI systems that power our hospitality analytics platform. You will work on revenue management and pricing problems in a complex, fast-moving domain, partnering closely with product, science, and engineering to turn ambiguous questions into rigorous analytical solutions. Your work will directly influence product direction and help hospitality customers make better, faster, more data-driven decisions.

What You Will Do:

• Build machine learning and AI solutions that support pricing, forecasting, and revenue management decisions in hospitality.
• Analyze large, sometimes messy datasets to identify patterns, risks, and opportunities, and turn them into practical recommendations or product capability.
• Write clean Python and SQL, working with data pipelines and analytical workflows to prepare, validate, and operationalize data.
• Apply modern AI techniques, including LLMs and AI coding tools such as Claude Code, thoughtfully to accelerate prototyping, learning, and development while maintaining technical quality.
• Partner closely with product, engineering, and cross-functional stakeholders to translate business problems into analytical solutions and communicate insights clearly.

What You Will Bring:

• 0–2 years of experience in data science, analytics, machine learning, or a related field, including internships, research, senior projects, or meaningful independent projects.
• Bachelor’s or Master’s degree in statistics, mathematics, operations research, computer science, engineering, or another quantitative field.
• Hands-on coding experience in Python, working knowledge of SQL, and familiarity with modern data analysis and machine learning workflows.
• Ability to structure ambiguous problems, choose sensible analytical approaches, and explain trade-offs, limitations, and recommendations clearly.
• Strong curiosity, ownership, and learning agility, with interest in hospitality, travel, pricing, forecasting, or other business problems where analytics can directly influence outcomes.

Preferred Qualifications:

Who We Are

A pioneer of Revenue Management, Revenue Analytics is an enterprise SaaS company that partners with hospitality, media, and transportation companies to solve their most complex pricing challenges. By leveraging powerful analytics and deep strategic experience, Revenue Analytics’ next-generation software delivers intuitive answers to help companies perfect their pricing, reclaim missed revenue, and take back their time. To learn more about how Revenue Analytics is recreating Revenue Management, visit revenueanalytics.com or follow us on Twitter and LinkedIn.

Visa sponsorship is not available for this role. We are committed to building a Team that represents a variety of backgrounds, perspectives, and skills. The more inclusive we are, the better our work will be.

• Experience with revenue management, pricing, forecasting, segmentation, or related analytical problem spaces.
• Experience building something reusable or scalable, such as a data pipeline, internal tool, model workflow, or decision-support analysis.
• Familiarity with cloud platforms, business intelligence tools, or modern AI tooling in technical workflows.