Data Scientist – Bayesian Hierarchical Modelling (R / Python / AWS)
Overview
We are seeking a highly capable Data Scientist with strong experience in Bayesian hierarchical modelling and advanced statistical techniques to join a growing data and analytics capability. This role sits across data science, data engineering, and backend development, supporting the delivery of scalable models, robust data pipelines, and high-quality insight products.
You will work with complex, high-volume datasets, applying statistical rigour to solve real business problems, while also contributing to the engineering layer that enables analytics at scale.
For a complete understanding of this opportunity, and what will be required to be a successful applicant, read on.
Key Responsibilities
- Design, build, and deploy Bayesian hierarchical models to support forecasting, inference, and decision-making
- Develop and maintain data pipelines and ETL processes, ensuring reliable, clean, and well-structured datasets
- Contribute to data “plumbing” and backend data services that support analytics and modelling workflows
- Work with large and complex datasets using Python and R
- Build and deploy scalable data solutions within AWS environments (e.g. S3, Glue, Lambda, Redshift, or equivalent services)
- Develop dashboards and data visualisations to translate complex model outputs into clear, actionable insights for stakeholders
- Support backend development where required, particularly around data APIs, pipelines, and integration layers
- Collaborate with data engineers, analysts, and business stakeholders to define requirements and deliver end-to-end solutions
- Ensure model performance, validation, monitoring, and continuous improvement
- Contribute to best practices across data science, engineering, and cloud-based data architecture
Key Skills & Experience
Essential
- Strong experience in Bayesian statistical modelling and hierarchical modelling techniques
- Proficiency in Python and R for data science and modelling
- Strong grounding in statistical modelling, probability, and inference methods
- Experience building and maintaining ETL pipelines and data workflows
- Experience with data engineering / data “plumbing” in cloud or distributed environments
- Working knowledge of AWS services (e.g. S3, Glue, Lambda, Redshift, or similar)
- Experience building dashboards using tools such as Power BI, Tableau, or similar
- Strong ability to manipulate, clean, and structure large datasets
- Ability to communicate complex analytical outputs in a clear and usable way
Desirable
- Exposure to backend development (APIs, services, or data layer engineering)
- Experience with probabilistic programming tools such as Stan or PyMC
- Experience operationalising data science models in production environments
- Familiarity with modern data stack tooling xwzovoh and cloud-native architectures
- Experience working in Agile delivery teams
- Exposure to real-time or large-scale data systems
Soft Skills
- Strong analytical and problem-solving capability
- Comfortable working across both engineering and analytical domains
- Strong stakeholder communication skills
- Ability to work independently and take ownership of delivery
- Commercial awareness and ability to translate data into business value
What This Role Offers
- Opportunity to work across full-stack data science and data engineering
- Exposure to advanced Bayesian modelling in a production environment
- Hands-on work with cloud infrastructure (AWS) and modern data pipelines
- Opportunity to shape how data is engineered, modelled, and consumed across the business
- High-impact role where statistical insight directly influences decision-making