Data Scientist

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Revenue Analytics, Inc.

WebsiteLinkedInAll Job Openings

51 - 200 employees

☁️ SaaS

🏢 Enterprise

🤝 B2B

SaaS • Enterprise • B2B

Revenue Analytics, Inc. is a company that specializes in providing advanced pricing and revenue management solutions for various industries, including hospitality, manufacturing, distribution, private equity, media, and passenger rail. They are pioneers in the field of Revenue Management, helping businesses make profitable pricing decisions at scale with their next-generation software. Their products, such as N2Pricing™, Pricing-as-a-Service™, RateOptics™, and FareVantage™, are designed to tackle complex pricing challenges and improve revenue outcomes for their clients. Revenue Analytics is trusted by top businesses to deliver confidence and data for making strategic pricing decisions.

📋 Description

• 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.

🎯 Requirements

• 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. • 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.