Axom is a venture-backed AI company building a marketing automation platform that helps businesses choose the next best action for each customer. We combine behavioural data, predictive models and decision optimisation to personalise customer interactions and deliver measurable commercial value.
Our existing clients include online casino operators, where high volumes of customer activity create complex, real-time decision problems. Our platform is designed to work across sectors, and we are expanding our partnerships to bring intelligent personalisation to other businesses and platforms.
Our next phase is agentic AI for next-best-action (NBA) systems: agents that can use models and tools, recommend or orchestrate marketing actions, and learn from outcomes and user feedback. We are developing this capability around clear business objectives, human oversight and reliable evaluation.
We move quickly, with a small, highly technical team and a practical approach to testing ideas and shipping improvements. You will work closely with colleagues across data science, engineering and product, with real ownership of the decisions our platform makes.
The role
We are looking for a Data Scientist with commercial experience, strong Python skills and practical machine learning experience to help improve the models and analysis behind Axom. You will join a talented and ambitious engineering team, contributing to established systems and learning new techniques as our product develops.
This is a hands-on role for someone who enjoys working with data and solving business problems. You will have ownership of defined projects, with support from the wider team on specialist modelling, production deployment and agentic AI development.
This is a hybrid role, working three days per week in the office.
What you will do
- Explore and prepare customer data, investigate patterns and help answer questions about product and client performance.
- Build, train and evaluate machine learning models, and improve existing models through feature engineering and validation.
- Contribute to our predictive models for customer lifetime value, churn and response to marketing actions, including the deep neural networks we use, with support from colleagues.
- Write clear, maintainable Python code and use AI coding agents to support development, reviewing and testing the code they produce.
- Work with engineers to put models and analysis into use, and help monitor their performance and investigate issues.
- Contribute to experiments and analysis that help us understand whether changes improve customer decisions and commercial outcomes.
- Contribute to our agentic AI and next-best-action capabilities alongside the wider team.
- Explain your findings clearly and collaborate with colleagues to turn analysis into practical product improvements.
What we are looking for
Must have
- At least 3 years of commercial work experience in data science, analytics, software development or a related technical role.
- A university degree in a technical subject, such as computer science, mathematics, statistics, physics or engineering.
- Strong Python skills, including practical experience using pandas to prepare, analyse and work with data.
- SQL skills and experience working with behavioural or transactional datasets.
- Practical experience applying supervised machine learning to real-world problems, including training models, evaluating performance and understanding overfitting and data leakage.
- Hands-on experience using AI coding agents or assistants, such as Codex, Claude, or Cursor, to write and improve code, with the ability to understand, review and test their output.
Nice to have
- Experience across different model types, including regression, random forests, gradient boosting models and neural networks.
- Experience with deep learning frameworks such as PyTorch or TensorFlow.
- Experience using Git, working through code review, or helping deploy and monitor machine learning models.
- Knowledge of statistics, A/B testing, experimental design or measuring incremental business impact.
- Experience with LLMs, AI agent workflows or frameworks such as LangChain or LangGraph.
- Experience with Azure, Databricks or Synapse, real-time analytics, or data visualisation tools such as Tableau or Power BI.
- Experience with customer lifetime value, churn, recommendation systems, or marketing optimisation.
- Familiarity with causal inference, uplift modelling, contextual bandits or reinforcement learning.
You do not need to have worked with every technology listed above. We are looking for strong core skills, clear communication, curiosity and a willingness to learn.
You will work on real business problems, see how your work influences customer decisions, and develop your skills alongside a highly technical team. As Axom expands its agentic AI capabilities, you will have opportunities to contribute and build experience in this area.
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