Senior Data Scientist

Multiverse is the upskilling platform for AI and Tech adoption.

We have partnered with 1500 companies to deliver a new kind of learning thats transforming todays workforce.

Our upskilling apprenticeships are designed for people of any age and career stage to build critical AI data and tech skills. Our learners have driven $2bn ROI for their employers using the skills theyve learned to improve productivity and measurable performance.

In April 2026 we announced $70 million in strategic funding led by Schroders Capital with participation from Step Stone Group Lightspeed Venture Partners and General Catalyst. At an increased valuation of $2.1bn the round makes us Europes first EdTech double unicorn.

But we arent stopping there. With a strong operational footprint and 800 employees we have ambitious plans to continue scaling. Were building a world where tech skills unlock peoples potential and output.

Join Multiverse and power our mission to equip the workforce to win in the AI era.

At Multiverse the models we build dont just sit in notebooks - they drive the decisions that shape our business every day. From predicting learner outcomes to forecasting operational demand and optimising how we allocate resources this work sits at the very core of how we run the company.

As a Senior Data Scientist youll own these models end to end. Youll develop a deep understanding of how Multiverse operates across our customer learner and operational domains - and translate that understanding into rigorous production-grade ML models that genuinely move the needle. To be successful youll be comfortable getting hands-on with pipelines and infrastructure - and unafraid of the statistical rigour that serious modelling demands.

Youll work closely with stakeholders across every part of the business - helping them ask better questions understand the answers and act on them with confidence. Our leaders will make multi-million dollar decisions based on your recommendations and our AI-powered product will decide how to support learners based on your models.

Youll sit within our Data & Insight team working day-to-day alongside Data Engineers Data Product Developers and Insight Analysts.

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

• With support from Data Engineering you can independently build and maintain the data pipelines and ML infrastructure needed to develop and deploy your models

• Product ionising 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

om collaborating with data engineering to highlight that we expect them be able to do some of this independently

Required

• 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. Num Py 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 Git Hub)

• 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…