Data Scientist

Job summary

Frame business problems as analytical or machine learning questions, analyze large datasets, build and evaluate predictive models, and design experiments. Collaborate on model deployment and monitoring, create clear visualizations, communicate findings, document work, and follow responsible data practices.

Job details

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

What you’ll do

Frame business problems as analytical or machine learning questions, analyze large datasets, build and evaluate predictive models, and design experiments. Collaborate on model deployment and monitoring, create clear visualizations, communicate findings, document work, and follow responsible data practices.

Requirements

The role seeks candidates with 0–5 years of relevant experience, strong Python or R and SQL skills, and a solid foundation in statistics, probability, and core machine learning concepts. Candidates should be able to visualize data and explain results clearly; experience with deep learning, cloud platforms, MLOps, Spark, causal inference, or a relevant advanced degree is desirable.

Listed skills

• Data visualization · Preferred
• SQL · Preferred
• Data analysis · Preferred
• Machine learning · Preferred
• Communication · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Python
• R
• SQL
• Statistics
• Probability
• Machine Learning
• Predictive Modeling
• Experimental Design
• Causal Inference
• Data Visualization
• Data Analysis
• A/B Testing
• Communication
• Model Evaluation
• Feature Engineering
• Responsible Data Practices

Job areas

• Data & Analytics
• Technology
• Science & Research
• Software

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