Data Scientist

Who we are 🩵

We’re the people behind global loyalty currency, Avios, and home to two ambitious, growing businesses across Loyalty and Holidays. Each business has its own goals, strategy and team, but collectively we share a purpose to
create the world’s most rewarding experiences
for our customers through loyalty programmes, new products and holidays

We’re on a truly exciting journey of growth and transformation – we’re going places! This is where you come in.

We’re looking for a Data Scientist with a few years of industry experience to join our team working on customer data. This role is ideal for someone who enjoys combining hands‑on machine learning with exploratory analysis, data visualisation, and business problem‑solving.

You’ll work closely with stakeholders to uncover meaningful insights, build predictive models, and help shape the future of our data and machine‑learning capabilities. Through a strong understanding of customer data and business needs, you’ll turn complex datasets into clear, actionable insights that support decision‑making and drive impact.

This is an opportunity to apply data science in a practical, outcomes‑focused environment, where your work directly influences how data and ML are used to solve real business problems and create value.

You’ll develop, evaluate and deploy machine learning models to support customer engagement, retention, churn prediction, and cross‑sell and up‑sell use cases. Alongside this, you’ll create clear and compelling visualisations and analytical outputs to communicate insights effectively to both technical and non‑technical stakeholders. Your work will span a range of projects, from hands‑on data science and modelling to more analytical and self‑serve initiatives.

You’ll work closely with engineers, analysts and business teams to translate business questions into practical data solutions, while maintaining high standards of data quality, validation and documentation throughout the delivery lifecycle. You’ll also contribute to improving data science best practice, tooling and ways of working, and show a strong interest in developing your machine learning engineering skills, including model deployment, monitoring and scalability.

We are aiming high, and we accept that it is unlikely that any one person will meet every aspect of the brief. Who you are is equally as important as what you have done or where you have worked.

So even if you don't tick every box, or your experience is from a unique or varied background, we'd still love to hear from you!

• Strong experience developing and evaluating machine learning models (e.g. classification, regression and clustering), with a solid grounding in statistical techniques
• Proficient in Python and common data science libraries such as pandas and scikit-learn
• Experience working with structured data, ideally customer or transactional datasets
• Confident using SQL to query data and working with data warehouses
• Able to take problems from initial ideation through to solutions that deliver clear business outcomes
• Comfortable working across modelling, analysis and data visualisation
• Familiar with cloud platforms and data pipelines
• Highly analytical, with a focus on improving customer interactions through data
• Strong attention to detail and a clear commitment to data quality
• Proactive, adaptable and comfortable shifting focus to where the greatest impact can be made
• Curious mindset with a genuine desire to learn, improve and develop over time
• Interest in growing towards a more machine learning engineering–focused skill set

Our vision, ‘to create the world’s most rewarding experiences,’ applies not only to our customers but for our colleagues too. It's about taking belonging seriously, actively fostering a culture where everyone feels welcomed and valued by embracing diverse identities, personal histories, and perspectives.

This commitment makes IAG Loyalty a rewarding place to work and enhances our ability to solve complex problems, drive innovation, and better serve our customers and communities.

Please let us know if we can make any reasonable adjustments to support your interview process with us.