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Sign Up to ReadAbout 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.
Part of the week in the office
Relocation offered
Visa sponsored
Company hidden
Office in Berlin, Germany
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