Data Science Manager - Experimentation Platform

Skyscanner; London; Hybrid; GBP 85,000 - 110,000; Full time

Job summary

A leading travel tech company in London is seeking a Data Science Manager to lead the data science team driving internal experimentation platforms. The candidate will develop tools and frameworks for confident decision-making across the business involving experimentation, statistical methods, and mentoring of team members. Expertise in Python, SQL, and statistical design is essential along with a collaborative mindset.

Qualifications

• Experience leading high-performing data science teams in experimentation or analytics.
• Deep knowledge of experimental design and Bayesian statistics.
• Proficient in Python and SQL with hands-on statistical programming experience.

Responsibilities

• Lead and grow a team of data scientists focused on experimentation enablement.
• Develop and maintain WISE, Skyscanner's experimentation library.
• Collaborate with product managers and engineers to define features.

Skills

Leadership

Experimental design

Statistical infrastructure

Python

SQL

Bayesian statistics

Causal inference

Anomaly detection

Forecasting

Mentoring

Tools

Airflow

Spark

GCP

AWS

Job description

Now, we're on the lookout for a Data Science Manager to lead the team powering our internal experimentation platform - a critical pillar of how we test, learn, and improve the travel experience for millions!
About the role

Hybrid

This is a high-impact leadership role focused on enabling smarter, faster decisions across Skyscanner. You'll lead the team behind WISE - our in-house experimentation platform - developing the tools, frameworks and statistical features that help teams across the business explore new ideas with confidence.

You'll work at the intersection of data science, engineering, and product, guiding both technical delivery and team growth. The scope extends beyond experimentation too - into areas like anomaly detection, forecasting, and bot detection - helping improve performance, reliability and decision quality at scale.
What you'll be doing

• Leading and growing a team of data scientists focused on experimentation enablement, internal analytics tooling, and measurement
• Developing and maintaining WISE, Skyscanner's experimentation library - ensuring statistical rigour, scalability, and usability across teams
• Collaborating with product managers and engineers to define features, deliver PoCs, and bring statistical tooling into production
• Designing advanced statistical features such as Bayesian methods, sequential testing and causal inference to support evolving experimentation needs
• Promoting experimentation best practices across the business - shaping how Skyscanner tests and learns
• Supporting internal measurement initiatives like anomaly detection, forecasting and LTV modelling - from ideation through to deployment
• Fostering a team culture grounded in learning, curiosity, and high standards in both statistical thinking and software development
• Representing the team in cross-functional forums, advocating for experimentation and internal tooling as key strategic enablers

About you

• Experienced leader: You've led high-performing data science teams, ideally in experimentation, statistical infrastructure or internal analytics platforms
• Statistically strong: You have deep knowledge of experimental design, Bayesian statistics and causal inference - and know how to apply them at scale
• Technically sharp: Proficient in Python and SQL, with hands-on experience in statistical programming. Familiarity with Airflow, Spark and cloud platforms (GCP/AWS) is a plus
• Platform-aware: You're comfortable working with engineers to bridge the gap between prototypes and production-grade tooling
• Measurement minded: You're fluent in the concepts that sit around experimentation - from anomaly detection to forecasting - and how they shape business performance
• Strategic stakeholder manager: You influence product direction, shape priorities, and communicate complex ideas clearly to varied audiences
• Supportive mentor: You're passionate about coaching others, creating a team environment that values autonomy, psychological safety and growth
• Balance focused: You're skilled at delivering near-term impact while investing in long-term platform quality, experimentation confidence and team health
• End-to-end thinker: You've worked across the full data science lifecycle - from exploration and measurement to production and iteration
• Sustainability and accessibility focused: You think in systems and solutions, always aiming for simplicity, inclusivity and long- term value.

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