Business Understanding & Problem Definition
Building genuine expertise in how Multiverse operates across customer, learner and operational domains
becoming a trusted thought partner
Translating complex and often ambiguous business questions into well-scoped modelling problems with clear success criteria
Identifying where predictive, forecasting or optimisation models can have the greatest business impact, and prioritising accordingly
Modelling & Statistical Analysis
Designing, developing and iterating supervised and unsupervised ML models that predict, forecast and optimise across the business
Applying rigorous statistical methods to ensure models are robust, unbiased and genuinely causal wherever causal claims are being made
Developing a deep understanding of our data landscape
its lineage, quirks, and limitations
and designing approaches that account for them
Monitoring and refining models over time, ensuring they remain accurate and relevant as the business evolves
Data Engineering & Infrastructure
Collaborating closely with Data Engineers to build and maintain the data pipelines and ML infrastructure needed to develop and deploy your models
Productionising models to run reliably at scale, adhering to software engineering best practices
including version control, CI/CD and vulnerability management
Evaluating and implementing scalable approaches to data collection and processing, ensuring robust practices are in place

5+ years of data science/machine learning experience, with a proven track record building and deploying models that drive real business decisions
Deep expertise in predictive modelling, forecasting and/or optimisation
with strong command of the underlying statistical principles
Strong proficiency in Python and core ML libraries (e.g., NumPy, Pandas, Scikit-Learn, xgboost, shap)
Advanced working knowledge of SQL
Hands-on experience with data pipelines and ML infrastructure
Experience working within AWS (ideally using Sagemaker) and/or Azure
Comfort working across our data stack
inc Airflow, Snowflake
Experience with version control and CI/CD practices (ideally using GitHub)
Rigorous attention to statistical validity
comfortable challenging assumptions and defending methodology
Understanding of best practices in data protection and information security
Desirable
Experience with causal inference methods (e.g., diff-in-diff, instrumental variables, propensity score matching)
Experience with dbt for data transformation
Knowledge of infrastructure as code tools (e.g. Terraform)
Strong professional and/or academic background within a highly quantitative discipline (e.g. statistics, mathematics, physics or economics)

Time off
27 days holiday, plus 5 additional days off: 1 life event day, 2 volunteer days, 2 company-wide wellbeing days (M-Powered Weekend) and 8 bank holidays per year
Health & Wellness
private medical Insurance with Bupa, a medical cashback scheme, life insurance, gym membership & wellness resources through Wellhub and access to Spill
all in one mental health support
Hybrid work offering
for most roles we collaborate in the office three days per week with the exception of Coaches and Instructors who collaborate in the office once a month
Work-from-anywhere scheme
you'll have the opportunity to work from anywhere, up to 10 days per year
Space to connect: Beyond the desk, we make time for weekly catch-ups, seasonal celebrations, and have a kitchen that’s always stocked!

Senior Data Scientist

Key skills