Senior Data Scientist

About the role

Multiverse is actively seeking a Senior Data Scientist to join the Data & Insight team in London and play a central role in shaping how the company leverages data and machine learning to drive real business outcomes. In this role, you will own the development, deployment, and lifecycle management of core predictive, forecasting, and optimization models that directly influence strategic decisions across the organization. You will act as a critical bridge between complex analytical work and tangible business value, translating ambiguous questions into well-defined modelling challenges with measurable success criteria. The position requires deep statistical rigor, hands-on comfort with production-grade infrastructure, and the ability to collaborate seamlessly with engineers and stakeholders to ensure models are robust, scalable, and reliable. You will become a trusted thought partner for leaders across customer, learner, and operational domains, using data to inform multi-million dollar decisions and product features that impact learners at scale.

Key facts

What you'll do

• Build genuine expertise in how Multiverse operates across customer, learner, and operational domains, becoming a trusted thought partner who can define problems with clarity and precision.
• Translate complex and often ambiguous business questions into well-scoped modelling initiatives with clearly defined success criteria and measurable impact.
• Identify where predictive, forecasting, or optimisation models can generate the greatest business value and prioritise efforts accordingly to maximise ROI.
• Design, develop, and iterate on supervised and unsupervised machine learning models that accurately predict, forecast, and optimise key business processes.
• Apply rigorous statistical methods to ensure models are robust, unbiased, and, where appropriate, support causal claims with defensible evidence.
• Develop a deep understanding of the company's data landscape, including lineage, quirks, and limitations, and design modelling approaches that account for these realities.
• Collaborate closely with Data Engineers and Data Product Developers to build and maintain the data pipelines and ML infrastructure required for development and deployment.
• Productionise models to run reliably at scale, adhering to software engineering best practices such as version control, CI/CD, and vulnerability management.
• Monitor model performance over time, refining and retraining as necessary to maintain accuracy and relevance as the business and its data evolve.
• Evaluate and implement scalable approaches to data collection and processing, ensuring robust data practices are embedded throughout the modelling lifecycle.
• Work hand-in-hand with stakeholders across every part of the business, helping them ask better questions, interpret results confidently, and act on insights with conviction.
• Champion the use of data-driven decision-making by ensuring that models not only perform technically, but also align with strategic objectives and operational realities.
• Lead the definition of data requirements and success metrics for new initiatives, ensuring that data infrastructure and models are built with scalability and maintainability in mind from day one.
• Contribute to the broader data strategy of the company, influencing standards, tooling choices, and best practices across the Data & Insight function.

Requirements

• 5+ years of data science or machine learning experience, with a proven track record of building and deploying models that drive real business decisions and measurable outcomes.
• Deep expertise in predictive modelling, forecasting, and/or optimisation, with a strong command of the underlying statistical principles and methodological foundations.
• Strong proficiency in Python and core machine learning libraries, including but not limited to NumPy, Pandas, Scikit-Learn, xgboost, and shap.
• Advanced working knowledge of SQL for data extraction, transformation, and exploratory analysis across large and complex datasets.
• Hands-on experience with data pipelines and machine learning infrastructure, including the ability to build and maintain reliable, production-grade systems.
• Experience working within cloud platforms, ideally AWS (including SageMaker) and/or Azure, for deploying and scaling models in real environments.
• Comfort working across a diverse data stack, including tools such as Airflow for orchestration and Snowflake for warehousing.
• Experience with version control and continuous integration and delivery practices, ideally using GitHub to manage code and collaboration.
• Rigorous attention to statistical validity, including the ability to challenge assumptions, defend methodology, and ensure models are both accurate and interpretable where required.
• Understanding of best practices in data protection and information security, ensuring that models and data handling comply with relevant policies and regulations.

Nice to have

• Experience with causal inference methods, such as difference-in-differences, instrumental variables, or propensity score matching, to strengthen claims about impact.
• Experience with dbt for data transformation, enabling modular, maintainable, and well-documented data pipelines.
• Knowledge of infrastructure as code tools, such as Terraform, to manage cloud environments and ensure reproducible deployments.
• Strong professional and academic background in quantitative disciplines, demonstrating a consistent record of applying data science to solve complex problems.

Practical notes

This role is based in London and is offered as a full-time position. The compensation details are not specified in the available source information.