Data Scientist
Role details
Job location
Tech stack
Job description
(Hybrid) Powering better decisions: You'll join our Decision Tooling team - the folks building the statistical and machine learning systems that help Skyscanner teams make smarter calls, faster. Making the complex feel clear: From forecasting demand to detecting anomalies and evaluating experiments, you'll help build reliable, interpretable systems people can actually trust - and act on. Raising the bar, together: You'll own well-defined components of larger modelling systems, while helping level up modelling standards across the team (think: quality, robustness, and "this will still work next quarter"). What you'll be doing Building decision-grade models: You will design and deliver ML and statistical systems that directly power real business decisions across Skyscanner. Turning ambiguity into frameworks: You will translate messy, open-ended problems into clear modelling approaches and crisp assumptions. Forecasting & anomaly detection: You will build and validate forecasting and anomaly detection solutions that spot issues early. Experimentation tooling: You will develop experimentation solutions that help teams evaluate change with confidence and clarity. Defining robust evaluation: You will set up backtesting, monitoring, and evaluation strategies that prove models are working - and keep them honest over time. Embedding into production workflows: You will partner closely with Engineering and Product to integrate models into production systems and day-to-day decision-making. Owning meaningful components: You will take ownership of well-scoped parts of larger systems and contribute to improving modelling standards across the team.
Requirements
Production-tested: You have 2+ years' experience as a Data Scientist working in a production environment. Applied ML experience: You've worked on real-world machine learning or applied data science problems (bonus points if you have worked on experimentation and casual inference). Strong fundamentals: You bring solid foundations in statistics, machine learning, or quantitative analysis, and, you know when to use which tool. Hands-on with Python & SQL: You're comfortable building in Python and querying data with SQL. Team-friendly engineer mindset: You're familiar with collaborative development practices like version control and code review. Structured problem-solver: You can take an ambiguous problem, break it down methodically, and move it forward without needing a perfect brief. Thoughtful evaluator: You care about robust evaluation, including fairness and reliability considerations.