You will join our Data Science team and help turn client data into reliable models and reusable analytics tooling. This is a hands-on modelling and engineering role: you will spend your time on data, code, and statistical methods.
- Prepare, clean, and quality-check datasets using SQL and Python or R
- Build and validate statistical models, including regression, machine learning, and forecasting
- Apply sound statistical thinking, including a working understanding of Bayesian ideas (priors, uncertainty, posterior interpretation)
- Automate recurring analysis so the team can reuse pipelines, functions, and tools
- Use Git to work with the team’s codebase
- Document methods and results clearly so others can review, rerun, and present the work
- 1–2 years in an Analytics or Data Science team
- Degree in Computer Science, Data Science, Statistics, Mathematics, Economics, or a related quantitative field
- Strong coding in Python or R (both is a plus)
- SQL
- Solid statistics: regression, machine learning fundamentals, and model validation
- Working understanding of Bayesian methodologies
- Comfortable with Git
- Fluent English, written and spoken
- Intellectually curious, organised, and able to work independently on well-scoped problems
Nice to have
- Hands-on Bayesian tooling (Stan, PyMC, brms, or similar)
- Cloud familiarity (AWS, Azure, or GCP)
- Experience in media, marketing, or marketing-effectiveness work
- Time series, causal inference, or test design
