Senior Data Scientist

🕒 June 4

🏢🏡 London – Hybrid

⏰ Full Time

🟠 Senior

📊 Data Scientist

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Multiverse

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2016

📚 Education

🏢 Enterprise

☁️ SaaS

Education • Enterprise • SaaS

Multiverse is a company dedicated to providing equitable access to economic opportunity through professional apprenticeships. It focuses on transforming education and the workforce by identifying, closing, and preventing skills gaps using a platform that leverages AI-powered tools. Multiverse operates by offering personalized learning pathways guided by expert coaches to improve productivity and performance. They work with enterprises to unleash employee potential by building data, AI, and software engineering skills, thereby fueling performance and business transformation. With a mission to make the workforce reflect societal diversity, they empower employees to explore new career paths while tracking measurable ROI of their learning strategies.

📋 Description

• 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 • 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

🎯 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 (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 • 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 economics)

🏖️ Benefits

• Time off - 27 days holiday, plus 5 additional days off: 1 life event day, 2 volunteer days, 2 company-wide wellbeing days (M-Powered Weekend) and 8 bank holidays per year • Health & Wellness- private medical Insurance with Bupa, a medical cashback scheme, life insurance, gym membership & wellness resources through Wellhub and access to Spill - all in one mental health support • Hybrid work offering - for most roles we collaborate in the office three days per week with the exception of Coaches and Instructors who collaborate in the office once a month • Work-from-anywhere scheme - you'll have the opportunity to work from anywhere, up to 10 days per year • Space to connect: Beyond the desk, we make time for weekly catch-ups, seasonal celebrations, and have a kitchen that’s always stocked!