We are seeking an experienced AI Data Engineer to design, develop, and deploy innovative data and machine learning solutions that drive customer engagement, retention, and lifetime value (LTV). This role will focus on building scalable data pipelines, predictive models, and GenAI-powered applications that enable more personalised trading experiences and support data-driven decision-making across the business.
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Key Responsibilities
- Collaborate with the wider marketing department to build meaningful datasets and a centralised data warehouse that will be used for reporting across numerous teams, focused on customer retention and LTV.
- Develop and own machine learning solutions that specialise in customer personalisation to increase engagement and coordinate with marketing teams towards their strategy and execution across multiple channels, especially around predictive modelling, churn prediction, and customer segmentation.
- Explore the capabilities of Gen AI to develop new apps for our traders that make trading easier and more personalised based on their preferences.
- Maintain a commercial acumen and find opportunities where technology can be used to increase customer engagement or automate internal tasks.
Qualifications & Requirements
- Strong expertise in SQL, comfortable with raw data and multiple high-volume datasets such as customer journey, marketing performance, or product behaviour.
- Comfortable creating pipelines and automations with Python to connect multiple endpoints and APIs such as first party databases, marketing platforms, or other data collection platforms.
- Expertise in using Python to deploy machine learning solutions end to end, especially around predictive modelling and customer segmentation.
- Previous experience in finding customer insights through data analysis, communicating it to different stakeholders and strategizing on how to increase customer engagement and LTV.
- Capable of creating applications using LLMs that summarise high volumes of data and knowledge of the latest Gen AI use cases.
- Previous exposure to ETL tools (e.g., dbt, AWS Glue) and cloud environments (preferably AWS, SageMaker).
- Strong communication skills with the ability to present complex data insights to non-technical stakeholders and support business strategy.
