Tripadvisor - Principal Data Scientist, Experimentation
Requirements
• Experience: Extensive experience in data science or a similar quantitative role, with a proven track record of supporting and influencing a product organisation. • Statistical & Experimentation Expertise: Authoritative command of experimentation in all its forms, from experimental design and variance reduction to causal inference, bandits and Bayesian methods. You should be able to develop and validate methodology, not only apply it. • Statistical & Experimentation Expertise: • Technical & Modelling Expertise: Expert level proficiency in Python and SQL. Deep, hands-on experience with statistical modelling, (quasi) experimentation, multi-arm bandits, and a wide range of machine learning techniques such as regression, classification and clustering. • Technical & Modelling Expertise: • Product Acumen: Demonstrated ability to define, implement and operationalise crucial product and feature-level metrics from scratch. • Product Acumen: • Partnership & Enablement: Demonstrated ability to improve how other teams work by providing guidance, tooling and protocols, increasing both the speed and the quality of their experimentation rather than absorbing the work yourself. • Partnership & Enablement: • Decision Impact & Platform Health: Ability to critically assess how product decisions affect platform health and growth over time, and to bring that perspective into how experiments are designed and interpreted. • Decision Impact & Platform Health: • Standardising at Scale: Experience standardising processes, frameworks or methods across multiple teams, including the use of AI and automation to make good practice the default. • Standardising at Scale: • Cross-Functional Partnership: Proven ability to build strong relationships and drive outcomes across Product, Engineering, Data Platform and other central functions, often without direct authority. • Cross-Functional Partnership: • Critical Thinking: Leader in critical thinking, with a demonstrated habit of establishing whether a result is trustworthy before establishing what it means. • Critical Thinking: • Communication: Exceptional ability to explain measurement, method and uncertainty clearly to technical and non-technical audiences at every level. • Communication: • Education: Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field. • Education: • You could be an especially great fit if you have: • Experience with experimentation in a two-sided marketplace, where interference between users and finite shared supply complicate measurement. • Experience building and validating proxy or surrogate metrics for long-horizon outcomes, and a clear view of how far they can be trusted. • Experience with Bayesian and hierarchical approaches, particularly for low-traffic surfaces or pooling evidence across many small markets. • Experience building or materially improving experimentation platforms, measurement frameworks, self-service capabilities or data products. • Experience in a high-scale technology company, marketplace, e-commerce business or travel technology organisation, and familiarity with the seasonality and long purchase cycles that come with them. • Breadth in statistical modelling and machine learning, such as regression, classification, clustering and bandits, alongside your experimentation depth. • Experience applying AI, Large Language Models, agentic AI or automation to improve analytical productivity and decision-making effectiveness. • A reputation for raising the standard of thinking and decision-making in every team you join. • Experience with SaaS experimentation tools such as Statsig, Eppo or GrowthBook, or with an in-house platform. • We strive to create an accessible and inclusive experience for all candidates. If you need a reasonable accommodation during the application or the recruiting process, please make sure to reach out to your individual recruiter or our team at [email protected]. • [email protected] • If you have any additional questions about careers at Tripadvisor you can email us at [email protected]. We have all the answers! • [email protected]
Responsibilities
• As a Principal Data Scientist you will be a leading individual contributor responsible for how experimentation works across the organisation. • You will lead through technical depth, setting the standards others work to and raising the quality of measurement and decision-making across the function. • You will build the capability that lets teams experiment well without central support: the standards, tooling and protocols that make good practice the default, and a strategy for how it develops over time. • Both the speed and the reliability of decision-making should improve as a result. You will also take on the measurement questions we cannot currently answer well, where traffic is thin or the outcomes that matter take months to appear. • Set the technical standard for experimentation across Product Data Science, from conventional A/B testing to quasi-experimental and Bayesian methods, and make it practical through protocols, frameworks and tooling. • Partner with Product, Engineering and Data Platform teams to improve experimentation velocity without sacrificing rigour. • Critically assess how experimentation and the decisions that follow it affect platform health and growth, and make that relationship visible to leadership. • Own the measurement approach for Viator's most complex questions, where standard experimentation is insufficient and the method has to be designed rather than selected. • Develop and validate the statistical methods the organisation relies on, including simulation-based verification that they behave correctly before teams depend on them. • Act as the final technical authority on measurement validity, adjudicating disputed results and determining what the evidence does and does not support. • Standardise recurring analytical and experimentation processes across the function, using automation and AI capabilities where they materially improve consistency, throughput or quality. • Design and land methodological improvements with organisation-wide impact, such as variance reduction for difficult metrics, approaches for low-traffic surfaces, proxy and surrogate metrics for long-horizon outcomes, and designs that hold up where users compete for shared supply. • Grow the technical depth of the wider team by reviewing designs, mentoring senior data scientists, and improving how people reason about measurement. • Advise on where experimentation is the wrong tool and define what should be done instead.
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