About the job
Are you a seasoned Data Analyst with a strong technical background and a passion for driving impactful decisions? Join us in enhancing the analytical framework that empowers our entire organization to make informed commercial and product choices.
At Vio, we are dedicated to securing the best accommodation deals for travelers around the globe. Central to this mission is our Offers Management Platform, a sophisticated system that determines which accommodation offers to request, store, and distribute across various channels and markets.
We are seeking a Senior Data Analyst to take charge of the analytics for this platform, collaborating closely with product managers and engineers to drive its evolution and optimization. This position focuses on understanding the system comprehensively, from end to end, establishing robust experimentation and measurement frameworks, and utilizing causal inference to steer our strategic decisions.
About the Role
You will be part of a cross-functional team overseeing the Offers Management Platform, which lies at the core of our marketplace. This system dictates which offers are requested from our suppliers, stored in our cache, and presented to end users or B2B partners, significantly influencing revenue and partner performance across the organization.
Your objective will be to render this system measurable and to enhance its performance.
You will engage closely with backend engineers and product managers on system design choices, establish metrics for success, and create analytical frameworks that allow us to analyze cause and effect in a highly interconnected environment.
Your Responsibilities
Lead Analytics for the Offers Management Platform
Cultivate an in-depth understanding of the processes through which offers are requested, stored, and distributed.
Translate intricate system behaviors into clear metrics and models.
Serve as the analytical authority for inquiries regarding the performance of the offers system.
Experimentation & Causal Inference
Design and implement experimentation strategies for system-level alterations where classic A/B testing is not applicable.
Employ causal inference techniques (e.g., difference-in-differences, propensity score matching) to assess the impact of changes.
