Revenue Analytics is a SaaS company that helps big companies make big revenue decisions in pricing, products and promotions. Our analytics solutions drive millions in revenue uplift and eliminate wasted time.
Founded in 2005, we leverage our deep experience in pricing and revenue management to empower our customers with predictive analytics engines that drive complex pricing decisions. Fueled by a recent capital infusion of $11 million, we are accelerating the use of these engines to power pricing solutions in four industry verticals – Media, Hospitality, Travel & Transportation, and Consumer & Industrial Products.
This Data Scientist position represents a unique opportunity to join a growth stage company that has essentially re-entered start-up mode. We’re seeking people excited about that opportunity who are looking to make things happen!
As a Data Scientist, you will play a critical role in analyzing and interpreting data to uncover insights that drive strategic decisions and operational improvements. You will work with cross-functional teams to leverage data in meaningful ways, from predictive analytics to customer segmentation, pricing optimization, demand forecasting, and more. Your work will directly impact the business by helping our partners in hospitality and travel make data-driven decisions that enhance their offerings and improve customer satisfaction.
What You Will Do:
Data Analysis & Modeling: Analyze large datasets from various sources (e.g., booking systems, customer feedback, operational data) to identify trends, patterns, and correlations. Build predictive models and algorithms that drive actionable business insights.
Forecasting & Demand Modeling: Develop models to forecast demand, occupancy rates, booking patterns, and other key metrics specific to the hospitality and travel industry, helping clients optimize pricing and resource allocation.
Customer Segmentation: Use clustering and segmentation techniques to create customer profiles, helping businesses tailor their offerings and marketing strategies to different customer segments.
Data Visualization: Create clear, intuitive visualizations and dashboards to present complex data in a way that is accessible to non-technical stakeholders, enabling better decision-making.
Collaboration: Work closely with product, marketing, and operations teams to ensure data solutions align with business objectives. Provide analytical support to improve customer experience, operational efficiency, and revenue generation.
Data Quality & Integrity: Ensure the quality and accuracy of data by developing processes for cleaning, validating, and transforming raw data into usable formats for analysis.
Reporting & Insights: Deliver regular reports and ad-hoc analysis to senior stakeholders, providing actionable insights to drive business strategies.
What You Will Bring:
1-3 years of experience in data science, analytics, or a similar field, with exposure to hospitality, travel, or related industries (e.g., tourism, transportation).
Proficiency in programming languages such as Python, R, or SQL for data analysis and modeling.
Experience with data visualization tools like Tableau, Power BI, or similar.
Familiarity with machine learning algorithms and statistical modeling techniques for forecasting, classification, and clustering.
Strong understanding of the hospitality or travel industry, including trends such as dynamic pricing, customer behavior analysis, and demand forecasting.
Ability to clean and manipulate large datasets, ensuring data integrity and accuracy.
Strong analytical thinking and problem-solving skills with a detail-oriented mindset.
Excellent communication skills, with the ability to explain complex data findings in a clear and actionable manner to both technical and non-technical audiences.
Master’s degree
Preferred Qualifications:
Experience working with cloud platforms (e.g., AWS, Google Cloud) and big data technologies (e.g., Hadoop, Spark) is a plus.
Familiarity with industry-specific tools (e.g., booking engines, CRM systems, travel data APIs).
A background in statistics, economics, or business analytics is highly valued.
