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# Senior Data Scientist

- **Company:** Multiverse
- **Location:** Greater London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Microsoft Azure, Cyber Security, Continuous Integration, Information Engineering, Data Transformation, Github, Python (Programming Language), Machine Learning, NumPy, Standard Sql, Software Vulnerability Management, Snowflake, Pandas, Scikit Learn, Infrastructure Automation Frameworks, Xgboost, Machine Learning Operations, Terraform, Software Version Control, Data Pipelines
- **Published:** September 10, 2026
- **Apply:** https://www.collegerecruiter.com/job/2840587137-senior-data-scientist

## About the Role

* 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, * 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)

## Description

* 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


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