Data Scientist

The Role

You'll work on problems such as [forecasting, personalisation, churn prediction, pricing, fraud detection, experimentation], partnering with product, engineering, analytics and business teams. You'll get strong mentoring and increasing ownership, whether you're early in your data science career or already have a few years of experience delivering impact.

What You'll Do

• Frame business problems as analytical or machine learning questions, and define success metrics
• Explore, clean and analyse large datasets to uncover patterns and opportunities
• Build, validate and tune predictive models (classification, regression, clustering, time series, NLP)
• Design and analyse experiments, including A/B tests and causal inference studies
• Work with data and ML engineers to deploy models and monitor performance in production
• Develop dashboards and visualisations that make results easy to understand
• Communicate findings and recommendations to technical and non-technical stakeholders
• Document methodologies, assumptions and results so work is reproducible
• Follow responsible data practices, including privacy, fairness and model explainability
• Keep up with new techniques and tools, and share learnings with the team

What We're Looking For

Essential

• 0 to 5 years' experience in data science, analytics, machine learning, research or a related role (strong projects, internships, research and career changers are welcome)
• Strong Python (or R) skills, including pandas, NumPy and scikit-learn
• Solid SQL skills
• Strong grounding in statistics and probability (hypothesis testing, regression, experimental design)
• Understanding of core ML concepts: model selection, feature engineering, evaluation metrics, overfitting
• Experience with data visualisation (matplotlib, seaborn, Plotly, Tableau or Power BI)
• Ability to explain technical results clearly to non-technical audiences
• Curiosity, critical thinking and attention to detail

Desirable

• Experience with deep learning frameworks (PyTorch, TensorFlow) or NLP and LLM tools
• Familiarity with cloud platforms (AWS, Azure, GCP) and data warehouses (Snowflake, BigQuery, Databricks)
• Experience with Git, Docker, MLflow or other MLOps tools
• Experience with Spark or large-scale data processing
• Knowledge of causal inference, Bayesian methods or optimisation
• Domain experience in [industry, e.g. finance, healthcare, retail, marketing]
• Master's or PhD in Statistics, Maths, Computer Science, Data Science, Physics, Economics or a related field, or equivalent practical experience