Senior Data Science Manager

About the role

This role leads the experimentation system for Viator and Tripadvisor, owning end-to-end health, quality, and maturity while providing technical leadership to product data science.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

Requirements

The posting states a bachelor's degree requirement. Extensive experience in data science, experimentation, product analytics, or a similar quantitative discipline is required, with a proven track record of improving experimentation systems, governance, or decision-making quality in a product-led organisation.

Strong proficiency in SQL and Python is required, along with deep understanding of A/B testing, statistical experimentation, causal inference, and applied experimentation frameworks.

Demonstrated ability to design, diagnose, and improve end-to-end experimentation systems, including governance, tooling, processes, and organisational behaviour is required.

Strong understanding of product development and decision-making is required, with the ability to influence prioritisation and roadmap decisions through experimentation insight.

Proven ability to influence senior stakeholders across Product, Engineering, and Data organisations without direct authority is required.

Experience improving experimentation velocity, quality, and adoption through structural interventions such as frameworks, automation, or system redesign is required.

Strong ability to evaluate experiment design, statistical validity, and interpretation of results, and to improve the thinking of others, is required.

Demonstrated ability to lead through influence and drive behavioural and organisational change across multiple teams is required.

Exceptional ability to communicate experimentation concepts clearly and confidently to technical and non-technical audiences is required.

Nice to have

Experience improving experimentation systems, governance, or operating models at scale rather than only running or analysing experiments is valued.

Experience driving experimentation adoption across multiple product teams, domains, or brands is valued.

Experience building experimentation frameworks, standards, or guardrails that improve quality and consistency is valued.

Experience reducing organisational dependency on central experimentation or data science teams through better system design and self-service enablement is valued.

Experience working in high-scale product environments such as marketplaces, e-commerce, or travel platforms is valued.

Experience working in SaaS experimentation tools such as Statsig, Eppo, Growthbook and others is valued.

A reputation for improving how organisations make decisions, not just the quality of individual analyses or experiments, is valued.

Practical notes

Work-life balance is supported by a flexible schedule.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

The role focuses on building and maintaining a robust experimentation system for a global travel marketplace.

Technical leadership is exercised through influence across product, engineering, and data teams.

Success depends on improving governance, standards, and scalability of experimentation practices.

The role works with high-volume marketplace data and complex user journeys in travel.

Collaboration is central, with frequent interaction with cross-functional stakeholders.

The team uses SQL, Python, and experimentation platforms to design, run, and interpret experiments at scale.