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
