About the Community
The Analytics Centre of Excellence (ACOE) at Airbnb, based in India, is a hub of knowledge and expertise that aims to provide data-driven decision-making, enabling Airbnb's business goals. The ACOE's vision is to build a world-class analytics organization that provides scalable analytics. We work with various business functions such as payments, trust, digital, customer support, hosting, sales, social, compliance, risk, platforms and partnership & economics.
The ACOE's delivery framework is designed to provide relevant and contextual insights for data-driven decisions. This includes a one-stop solution for metrics, dashboards driving actionable insights, optimisation of performance, measuring pre/post feature impact, sizing the ROI of opportunities, prioritisation of opportunities, anomaly-driven alerting mechanisms, root cause analysis of metric deviation, and exploratory hypothesis testing.
About the Role
We are looking for a hands-on lead advanced analyst who has demonstrated success in building an analytics team with functional expertise in building analytical solutions. Be a trusted partner to product teams and business leaders, across marketplace teams through providing insights, recommendations, and enabling data driven decisions. This role will sit under the Advanced Analytics family and support Product and Business leaders within our CS organization.
Responsibilities
- Data thought partner to product and business leaders across marketplace teams through providing insights, recommendations, and enabling data informed decisions.
- Drive day to day product analytics and build scalable analytical solutions.
- Develop a deep understanding of how guests and hosts interact with our CS products including but not limited to AI Assistant, IVR and other agent assisting tools to improve the customer experience.
- Owning the product analytics roadmap, prioritisation & delivery of solutions in the Contact Center Product space.
- Own projects from start to end: building out timelines, key milestones, providing regular updates to product managers, analytics management and delivery against agreed timelines.
- Lead end-to-end measurement for AI systems, aligning metrics with business outcomes, user experience, and trustworthiness.
- Collaborate with engineering to define scalable logging and ensure observability across agentic and LLM workflows.
- Lead the design and evaluate A/B and causal tests to quantify impact of AI features and optimisations.
- Translate insights into strategic recommendations for senior leaders; shape product priorities through data.
- Hire, mentor and up-level team capability in AI analytics, experiment best practices and storytelling.
Requirements
- Min 8 or more years in industry experience and a degree (Masters or PhD is a plus) in a quantitative field (e.g., Statistics, Econometrics, Computer Science, Engineering, Mathematics, Data Science, Operations Research).
- Expert communication and collaboration skills with the ability to work effectively with internal teams in a cross-cultural and cross-functional environment. Ability to conduct rigorous analysis and communicate conclusions to both technical and non-technical audiences.
- Strong expertise in Python, SQL, A/B testing platforms and best practices.
- Expertise in EDA, hypothesis testing, significance testing, regression, clustering techniques, concepts of NLP/Text Mining, machine learning and deep learning techniques, language model fine-tuning etc.
- Understanding of LLM architectures (e.g., prompt chains, retrieval augmentation, orchestration frameworks like LangChain or DSPy).
- Data engineering foundations, including ability to work with data engineering teams on pipeline design, metric computation jobs, data model changes, and maintaining reliable end-to-end metric systems.
- Familiarity with Agentic AI systems, human-in-the-loop design, and AI observability best practices.
- Experience partnering with internal teams to drive action and providing expertise and direction on analytics, data science, experimental design, and measurement.
- Experience designing and building metrics, from conception to building prototypes with data pipelines.
- Familiarity with vector stores, embeddings, prompt instrumentation, and structured logging of LLM interactions would be a plus.
- Contact center domain and market place domain knowledge would be a plus.