Senior Data Scientist

Company: Multiverse

Location: UK (Hybrid work offering, with work-from-anywhere options)

About Multiverse:
Multiverse is a leading upskilling platform for AI and Tech adoption, partnering with over 1,500 companies to deliver transformative learning experiences. Our apprenticeships empower individuals of all ages and career stages with critical AI, data, and tech skills, having driven over $2 billion in ROI for employers. As the UK's first EdTech unicorn, we are scaling rapidly with ambitious plans to equip the workforce for the AI era.

The Role:
As a Senior Data Scientist at Multiverse, you will own the end-to-end development of machine learning models that drive critical business decisions, from predicting learner outcomes to forecasting operational demand and optimizing resource allocation. You will translate business understanding into rigorous, production-grade ML models, collaborating closely with stakeholders across the business. Your work will directly influence multi-million dollar decisions and power our AI-powered products.

You will be part of the Data & Insight team, working alongside Data Engineers, Data Product Developers, and Insight Analysts.

What You'll Focus On:

• Business Understanding & Problem Definition: Develop expertise in Multiverse's operations, translate ambiguous business questions into scoped modeling problems, and identify opportunities for predictive, forecasting, or optimization models.
• Modelling & Statistical Analysis: Design, develop, and iterate on supervised and unsupervised ML models. Apply rigorous statistical methods to ensure robustness and causality. Understand and leverage our data landscape, monitoring and refining models over time.
• Data Engineering & Infrastructure: Collaborate with Data Engineers on data pipelines and ML infrastructure. Productionize models adhering to software engineering best practices (version control, CI/CD). Evaluate and implement scalable data collection and processing approaches.

What We're Looking For:

Required:

• 5+ years of data science/machine learning experience with a proven track record of deploying models that drive business decisions.
• Deep expertise in predictive modeling, forecasting, and/or optimization, with a strong command of 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 SageMaker) and/or Azure.
• Comfort working across our data stack (e.g., Airflow, Snowflake).
• Experience with version control and CI/CD practices (e.g., GitHub).
• Rigorous attention to statistical validity.
• Understanding of data protection and information security best practices.

Desirable:

• Experience with causal inference methods.
• Experience with dbt for data transformation.
• Knowledge of infrastructure as code tools (e.g., Terraform).
• Strong quantitative background (statistics, mathematics, physics, economics).

Benefits:

• Time Off: 27 days holiday + 5 additional days (life event, volunteer, wellbeing, bank holidays).
• Health & Wellness: Private medical insurance (Bupa), medical cashback, life insurance, gym membership (Wellhub), mental health support (Spill).
• Hybrid Work: Collaborate in the office three days per week (most roles).
• Work-from-Anywhere: Up to 10 days per year.
• Connection: Weekly catch-ups, seasonal celebrations, stocked kitchen.