What we need
At Multiverse, the models we build don't 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, you'll own these models end to end. You'll 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, you'll be comfortable getting hands‑on with pipelines and infrastructure - and unafraid of the statistical rigour that serious modelling demands.
You'll 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.
You'll sit within our Data & Insight team, working day‑to‑day alongside Data Engineers, Data Product Developers and Insight Analysts.
What you’ll focus on
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
What we’re looking for
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., 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!
Our Commitment to Diversity, Equity and Inclusion
We’re an equal opportunities employer. And proud of it. Every applicant and employee is afforded the same opportunities regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. This will never change. Read our Equality, Diversity & Inclusion policy here.
Our Commitment to Safeguarding
Multiverse is committed to safeguarding and promoting the welfare of our learners. We expect all employees to share this commitment and adhere to our Safeguarding Policy, our Prevent Policy and all other Multiverse company policies. Successful applicants will be required to undertake at least a Basic check via the Disclosure Barring Service (DBS).
For roles that will involve a Regulated Activity, successful applicants must also undergo an Enhanced DBS check, including a Children’s Barred List check and a Prohibition Order check. Roles involving Regulated Activity may interact with vulnerable groups, therefore are exempt from the Rehabilitation of Offenders Act 1974 meaning applicants are required to declare any convictions, cautions, reprimands, and final warnings.
Providing false information is an offence and could result in the application being rejected or summary dismissal if the applicant has been selected, and possible referral to the police and the DBS.
#J-18808-Ljbffr