Senior Data Scientist

About the opportunity

Who we are

N26 has reimagined banking for today’s digital world. Technology and design empower everything we do and it’s how we are building the global banking platform the world loves to use.

We've eliminated physical branches, paperwork, and hidden fees for an elegant digital experience and supreme savings. Giving people the power to live and bank their way is what gets us out of bed in the morning and inspires the work that we do.

We are headquartered in Berlin with offices in multiple cities across Europe, including Vienna and Barcelona, and a 1,500-strong team of more than 80 nationalities.

• We are seeking a Senior Data Scientist to build production-ready applications. Our Data Science team develops practical machine learning applications to enable innovative and customer-facing product features.
• Have end-to-end responsibility, from data exploration to applying state-of-the-art machine learning techniques and building production-ready applications.
• Your work will directly enable innovative solutions in financial crime prevention and credit risk assessment.
• Drive your project and work with experienced data scientists, machine learning engineers, and product managers to deliver customer value and allow you to develop your skill-set further.
• Have the opportunity to experiment with new tech stacks and project ideas.

• Bachelor’s or Master’s degree with a focus on quantitative disciplines including mathematics, statistics, or computer science.
• Proven record of implementations in the prototyping, development, and productionizing of machine learning solutions, particularly in banking applications such as fraud detection and credit risk.
• Deep, hands-on experience with a variety of modeling techniques, including:
• Classification models: (e.g., CatBoost, XGBoost, LightGBM)
• 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)
• Strong programming skills, specifically in Python and SQL.
• Basic infrastructure knowledge is needed (e.g., microservices, Docker/containers, CI/CD, GitHub Actions).
• Foundational knowledge of Deep Learning is needed, including an understanding of neural networks, transformers, and autoencoders, and experience with frameworks like TensorFlow, Keras, or PyTorch.
• Good understanding of common machine learning algorithms and a proven ability to teach and mentor junior data scientists.
• You are creative, organised, self-motivated, and able to drive ambitious data science projects to success from conception through coding to deployment.
• Language skills: English (full professional proficiency).
• Preferred Qualifications & 'Nice to Haves'
• Good knowledge and experience in the banking industry is a major plus (regulatory and compliance, provisions and capital requirements, PD/LGD/EAD modelization, credit card fraud, anti-money laundry modelization).
• Experience with cloud ML platforms, specifically AWS Sagemaker, is a plus.
• Knowledge of Large Language Models (LLMs) is a plus.
• 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.