About this role
About the opportunity
We are seeking a Data Scientist to build production-ready applications. Our Data Science team develops practical machine learning applications to enable innovative and customer-facing product features.
In this role, you will:
• Own well-scoped projects from data exploration and feature engineering through modelling to deployment, with guidance from senior colleagues on larger or more ambiguous problems.
• Contribute to solutions in financial crime prevention and credit risk assessment that reach customers.
• Collaborate with data scientists, machine learning engineers and product managers to deliver customer value.
• Support junior colleagues through code reviews, pairing and knowledge sharing.
• Have the opportunity to experiment with new tech stacks and project ideas.
What you need to be successful (Background and Skills):
• Bachelor’s or Master’s degree with a focus on quantitative disciplines including mathematics, statistics, or computer science.
• Practical experience (typically 2–4 years) developing machine learning solutions, with at least one model taken to production. Experience in banking, fraud detection, or credit risk is a plus.
• Hands-on experience with a range of modelling techniques, including:
• Classification and regression models: (e.g., CatBoost, XGBoost, LightGBM)
• Time Series Analysis: (e.g., ARIMA, SARIMA, Prophet)
• Unsupervised Models: (e.g., clustering, anomaly detection such as Isolation Forest)
• Solid understanding of model evaluation, including handling imbalanced data, validation strategies, and calibration.
• Strong programming skills in Python and SQL.
• Working knowledge of infrastructure (e.g., Docker/containers, CI/CD, GitHub Actions) and the ability to ship and maintain your own code.
• Familiarity with Deep Learning concepts (neural networks, transformers, autoencoders) and at least one framework (TensorFlow, Keras, or PyTorch).
• You are organised and self-motivated, and can drive projects forward independently while knowing when to ask for input.
• Language skills: English (full professional proficiency).
Preferred Qualifications & "Nice to Haves"
• Knowledge of the banking industry (regulatory and compliance, PD/LGD/EAD modelling, credit card fraud, anti-money laundering).
• Experience with cloud ML platforms, especially AWS SageMaker.
• Familiarity with Large Language Models (LLMs).
• Early experience mentoring or onboarding colleagues.
Traits
• Highly collaborative and actively help yourself (and others) be successful.
• A strong passion for learning and continuously challenging the status quo.
• Strong bias for action and a willingness to make an impact from day 0.
• Give and receive open, direct, and timely feedback.
• Think globally, act locally.
What’s in it for you:
• Accelerate your career growth by joining one of Europe’s most talked about disruptors.
• Employee benefits that range from a competitive personal development budg