Senior Data Scientist

Salary: £67,000 - 107,000 per year

Requirements:

• 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 such as NumPy, Pandas, Scikit-Learn, xgboost, and 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, including Airflow and Snowflake
• Experience with version control and CI/CD practices, ideally using GitHub
• Rigorous attention to statistical validity, with the ability to challenge assumptions and defend methodology
• Understanding of best practices in data protection and information security
• Experience with causal inference methods such as diff-in-diff, instrumental variables, and propensity score matching
• Experience with dbt for data transformation
• Knowledge of infrastructure as code tools such as Terraform
• Strong professional and/or academic background within a highly quantitative discipline such as statistics, mathematics, physics, or economics

Responsibilities:

• Own models end to end that predict learner outcomes, forecast operational demand, and optimise resource allocation
• Develop a deep understanding of how we operate across customer, learner, and operational domains, and translate that understanding into production-grade ML models
• Work closely with stakeholders across the business to help them ask better questions, understand the answers, and act on them with confidence
• Become a trusted thought partner by building genuine expertise in how we operate across customer, learner, and operational domains
• Translate complex and often ambiguous business questions into well-scoped modelling problems with clear success criteria
• Identify where predictive, forecasting, or optimisation models can have the greatest business impact and prioritise accordingly
• Design, develop, and iterate supervised and unsupervised ML models that predict, forecast, and optimise across the business
• Apply rigorous statistical methods to ensure models are robust, unbiased, and genuinely causal where causal claims are being made
• Develop a deep understanding of our data landscape, including lineage, quirks, and limitations, and design approaches that account for them
• Monitor and refine models over time to ensure they remain accurate and relevant as the business evolves
• Independently build and maintain the data pipelines and ML infrastructure needed to develop and deploy models, with support from Data Engineering as needed
• Productionise models to run reliably at scale, adhering to software engineering best practices including version control, CI/CD, and vulnerability management
• Evaluate and implement scalable approaches to data collection and processing, ensuring robust practices are in place

Technologies:

• AI
• Airflow
• AWS
• Azure
• CI/CD
• GitHub
• Support
• Machine Learning
• Python
• SQL
• Security
• Snowflake
• Terraform
• dbt
• numpy
• pandas
• Cloud

More:

We are Multiverse, the upskilling platform for AI and tech adoption. We have partnered with 1,500+ companies to deliver learning that is transforming todays workforce, and our apprenticeships help people of any age and career stage build critical AI, data, and tech skills. Our learners have driven $2bn+ ROI for their employers. In April 2026, we announced $70 million in strategic funding led by Schroders Capital, with participation from StepStone Group, Lightspeed Venture Partners, and General Catalyst, taking us to an increased valuation of $2.1bn as Europes first EdTech double unicorn. With 800+ employees and ambitious plans to keep scaling, we are building a world where tech skills unlock peoples potential and output. You will join our Data & Insight team in our London-based, hybrid Engineering function, with most roles collaborating in the office three days per week. We offer 27 days holiday plus 5 additional days off, private medical insurance with Bupa, a medical cashback scheme, life insurance, gym membership and wellness resources through Wellhub, access to Spill for mental health support, a work-from-anywhere scheme for up to 10 days per year, and regular opportunities to connect through catch-ups and seasonal celebrations.

last updated 35 week of 2026