As a Junior Data Scientist, you will contribute to the development and delivery of data science products, working alongside senior data scientists. You will be involved in implementing and refining supervised learning, bandit algorithms, and generative AI models, as well as supporting
experimentation and analysis.
You will write production-quality Python code, collaborate on cloud-based deployments, and help translate data insights into actionable recommendations that drive business impact. This role provides hands-on experience while allowing you to take ownership of well-scoped components
within larger projects.
This is a fantastic opportunity for an early-career data scientist with an analytical background to join and grow within a market leading digital content agency and media network.
CORE RESPONSIBILITIES
Model Development: Assist in developing, testing, and improving machine learning
models, with a focus on bandit algorithms and experimentation frameworks.
Experimentation: Support the setup, execution, and analysis of A/B tests and online experiments to evaluate the impact of our generative AI-driven products.
Production Support: Assist with deploying and monitoring models and experiments on GCP (Airflow, Docker, Cloud Run, SQL databases, etc.), following existing patterns and CI/CD workflows.
Data Analysis: Perform exploratory data analysis, data validation, and basic feature engineering to support modelling and experimentation efforts.
Collaboration: Work closely with senior data scientists, engineers, and product stakeholders to understand business problems and translate them into actionable tasks.
SKILLS REQUIRED FOR THIS ROLE
Essential Functional/Job-specific skills
- Bachelor’s or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or
a related field with 1+ years of relevant work experience.
- Solid foundation in SQL and Python, including experience with common libraries such as
Pandas, NumPy, Scikit-learn, Matplotlib, and Statsmodels.
- Basic understanding of supervised learning, experimentation, causal inference, and concepts in
reinforcement learning and multi-armed bandits.
- Foundational knowledge of probability, statistics, and linear algebra.
- Working knowledge of Git, including version control and collaboration through pull requests and
code reviews.
- Ability to write good documentation and ability to explain analysis results clearly to technical
and non-technical audiences
- Familiarity with deploying machine learning models in production cloud environments (GCP or
AWS).
Essential core skills
Communication
Collaboration
Organisation
Delivering Results
Solutions Focused
Adaptability