Senior Data Scientist

ROLE OVERVIEW

Role includes the development and application of transformer-based foundation models that learn from large-scale customer-event sequences. The role combines deep technical ownership with project leadership: shaping modelling strategy, guiding junior team members, managing stakeholder expectations and ensuring that solutions are validated, explainable and ready for governed implementation. The successful candidate will remain hands-on while setting standards for model quality and delivery.

KEY RESPONSIBILITIES

• Define the modelling roadmap for self-supervised pretraining, representation learning and downstream adaptation across fraud, delinquency, acquisition and behavioural-risk use cases.
• Lead architecture choices, training strategy, evaluation design and benchmarking against strong classical ML baselines.
• Guide approaches for multi-label learning, task-specific heads, attention pooling, embeddings and fine-tuning while controlling leakage and overfitting.
• Own end-to-end model validation, including out-of-time performance, stability, drift, explainability and limitations.
• Translate business objectives into analytical workplans, milestones, resource requirements, risks and measurable success criteria.
• Partner with ML engineering, data, cloud, model validation and business teams to move models from research into controlled implementation.
• Communicate technical trade-offs and results to senior stakeholders; prepare governance-ready methodology, validation responses and decision materials.
• Monitor delivery against plan, resolve dependencies and escalate material risks with clear mitigation options.

REQUIRED SKILLS AND EXPERIENCE

Programming:

Deep learning:

Modelling:

Validation:

Data:

Ways of working:

GOOD TO HAVE

• Credit risk, fraud, collections, underwriting or financial-services analytics experience.
• Experience building foundation models for tabular, behavioural, recommendation or event data rather than only text.
• Large-scale distributed training, model compression, serving optimisation or real-time inference.
• Model risk governance, independent validation interaction or deployment in a regulated environment.

QUALIFICATIONS

• Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Physics, Computer Science, Engineering or another quantitative field; advanced research qualifications are welcome.
• 4–8 years of applied machine-learning experience with strong hands-on depth in deep learning and transformers.

Demonstrated ownership of complex analytical deliveries and leadership of technical teams or workstreams.