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Applied AI Scientist
Skills
Artificial IntelligenceBERTCausal InferenceDeep LearningMachine LearningPropensity ModellingPytorch
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Strong background in Machine Learning Deep Learning and Applied AI Research
- Hands-on experience with PyTorch and or TensorFlow
- Strong understanding of transformer architectures embeddings and foundation model techniques
- Experience building predictive models recommendation systems propensity models customer intelligence solutions or behavioural models
- Strong experimental design and model evaluation experience
- Ability to translate business problems into scalable machine learning solutions
- Experience working with large datasets and representation learning approaches
- Strong communication skills and ability to explain complex concepts to technical and non-technical audiences
Day to day
- Frame complex business challenges as machine learning and AI problems.
- Research prototype and evaluate advanced modelling approaches including foundation models transformer architectures embeddings and representation learning.
- Design and develop a customer foundation model that supports multiple downstream use cases through a shared customer representation.
- Build customer embeddings and behavioural representations to power personalisation targeting propensity modelling and decisioning capabilities.
- Design robust experiments and validate model performance through statistical analysis benchmarking and evaluation frameworks.
- Work with large-scale customer datasets and collaborate with cross-functional teams to move research into production.