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Omaze is a UK and Germany-based platform that gives communities the chance to win luxury homes and life-changing prizes while raising money for charitable causes. The company has raised over £150 million for UK charities including Age UK, the RSPCA, British Heart Foundation, and Great Ormond Street Hospital Charity.
As Senior Data Scientist, you will be a hands-on member of the Data & Analytics team, working across Omaze's core commercial and product domains. You will design, build, and deploy predictive models and statistical frameworks that directly inform customer acquisition, conversion, retention, and lifetime value understanding.
Key responsibilities include:
- Building, validating, and iterating on predictive models for churn, conversion, customer lifetime value, and customer segmentation using tabular machine-learning techniques (gradient-boosted trees, logistic regression, survival models)
- Owning statistical methodology for the experimentation programme, including power analysis, test design, sequential testing, multiple-comparison corrections, and pre-registered decision frameworks
- Developing propensity and uplift models to support targeting, personalisation, and marketing effectiveness measurement
- Writing production-quality code in Python and/or R, partnering with Data Engineering to deploy models into the data warehouse and downstream systems
- Translating commercial questions from Product and Marketing teams into well-scoped analytical and modelling problems
- Communicating model outputs, experiment results, and recommendations clearly to technical and non-technical audiences
- Contributing to Data & Analytics team standards, including code review, documentation, and reproducibility
- Building models that are technically robust and operationally useful, with focus on measurable impact
You will work closely with Product, Marketing, Commercial, and Engineering teams to turn complex data into clear decisions and useful solutions.
REQUIREMENTS:
- Proven experience in applied data science, machine learning, or quantitative analytics
- Strong hands-on experience building supervised learning models using real-world tabular data (classification, regression, survival analysis)
- Experience with gradient-boosted tree frameworks (XGBoost, LightGBM, CatBoost)
- Strong grounding in experimental design and statistical inference (hypothesis testing, confidence intervals, power analysis, multiple-testing corrections)
- Experience designing and analysing A/B tests; sequential testing or Bayesian approaches a bonus
- Proficiency in Python and/or R with experience in feature engineering, model training, evaluation, and deployment
- Strong SQL skills and experience with large-scale data warehouses and cloud data platforms (Snowflake, BigQuery, Redshift)
- Understanding of model validation approaches (cross-validation, time-based splits) and ability to manage challenges (missing data, class imbalance, data leakage, changing distributions)
- Understanding of causal inference concepts (difference-in-differences, propensity score matching, uplift modelling)
- Familiarity with customer analytics concepts (churn, lifetime value, cohort analysis, retention)
- Ability to distinguish between statistical and practical significance and explain complex findings to non-technical stakeholders
- Collaborative and pragmatic approach, focused on shipping useful, high-quality solutions
- Experience in subscription, e-commerce, or marketplace business, or with ML pipeline and model deployment tooling, is a plus
- Degree in Statistics, Mathematics, Computer Science, Economics, Physics, or another quantitative discipline is useful; Master's or PhD desirable but not required
