Omaze
Senior Data Scientist
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Senior Data Scientist
đź“Ť Location - London
Who We Are:
At Omaze, we give our community in the UK and Germany the chance to win luxury homes and other life-changing prizes — all while raising money for the causes they love.
Thanks to our Omaze Community, we've raised over ÂŁ150 million for UK charities. That's millions helping organisations like Age UK, the RSPCA, British Heart Foundation and Great Ormond Street Hospital Charity deliver life-saving work.
And the best part? We're only just getting started. Omaze is building a business and culture committed to growth and creating significant social impact on a global scale.
About The Job
As our Senior Data Scientist, you’ll be a hands-on member of our Data & Analytics team, working across Omaze’s core commercial and product domains. You’ll design, build and deploy predictive models and statistical frameworks that directly inform how we acquire, convert and retain customers, and how we understand their long-term value.
You’ll own end-to-end model development across areas including subscriber churn, site and funnel conversion, customer lifetime value and propensity modelling for segmentation and targeting.
You’ll also play a central role in strengthening our experimentation programme, helping us design robust A/B and multivariate tests and ensuring we apply the right statistical methodologies to interpret and act on results with confidence.
Working closely with Product, Marketing, Commercial and Engineering, you’ll turn complex data into clear decisions and useful solutions — ultimately helping Omaze become number one for charitable impact globally.
What You’ll Be Doing
- Build, validate and iterate on predictive models for churn, conversion, customer lifetime value and customer segmentation using tabular machine-learning techniques, including gradient-boosted trees, logistic regression and survival models.
- Own statistical methodology for our experimentation programme, including power analysis, test design, sequential testing, multiple-comparison corrections and pre-registered decision frameworks.
- Develop propensity and uplift models to support targeting, personalisation and marketing effectiveness measurement.
- Write production-quality code in Python and/or R, partnering with Data Engineering to deploy models into our data warehouse and downstream systems.
- Work with Product and Marketing teams to translate commercial questions into well-scoped analytical and modelling problems.
- Communicate model outputs, experiment results and recommendations clearly to technical and non-technical audiences, helping stakeholders understand both statistical and practical significance.
- Contribute to how the Data & Analytics team works, including code review standards, documentation and reproducibility.
- Build models that are not only technically robust, but operationally useful — with a focus on delivering measurable impact for our customers, our business and our charity partners.
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About You
- Proven experience in applied data science, machine learning or quantitative analytics.
- Strong hands-on experience building supervised learning models using real-world tabular data, including classification, regression and/or survival analysis.
- Experience with gradient-boosted tree frameworks such as XGBoost, LightGBM or CatBoost.
- Strong grounding in experimental design and statistical inference, including hypothesis testing, confidence intervals, power analysis and multiple-testing corrections.
- Experience designing and analysing A/B tests; experience with sequential testing or Bayesian approaches would be a bonus.
- Proficient in Python and/or R and comfortable working across feature engineering, model training, evaluation and deployment.
- Strong SQL skills and experience working with large-scale data warehouses and cloud data platforms such as Snowflake, BigQuery or Redshift.
- Understanding of model validation approaches such as cross-validation and time-based splits, and know how to manage challenges including missing data, class imbalance, data leakage and changing distributions.
- Understanding of causal inference concepts such as difference-in-differences, propensity score matching and uplift modelling, and can identify when causal or predictive approaches are most appropriate.
- Comfortable with customer analytics concepts such as churn, lifetime value, cohort analysis and retention.
- Can distinguish between statistical and practical significance and explain complex findings clearly to non-technical stakeholders.
- Collaborative and pragmatic, with a focus on shipping useful, high-quality solutions rather than pursuing theoretical perfection.
- Experience in a subscription, e-commerce or marketplace business, or with ML pipeline and model deployment tooling, would be a plus.
- Degree in Statistics, Mathematics, Computer Science, Economics, Physics or another quantitative discipline is useful; a Master’s or PhD is desirable but not required.
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What’s In It For You
- Generous stock options scheme
- 25 days annual leave PLUS Bank Holidays
- Private medical and dental insurance
- 9% employer pension contributions, when you contribute at least 2%
- A generous personal learning and development budget each year to use on training courses, conferences and professional memberships
- Enhanced family leave policies
- Life assurance of 4x your salary
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