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Fraud Data Scientist

Billie -
Berlin

Jetzt bewerben

Details zur Stelle

Vollzeit

Leistungen

Homeoffice-Möglichkeit
Firmenevents
Sabbatical
Flexible Arbeitszeiten
Kostenfreies oder vergünstigtes Deutschland-Ticket

Qualifikationen

Kubernetes
Master
SQL
Docker
Machine Learning
PostgreSQL
Deep learning
Apache
Python
MySQL

Vollständige Stellenbeschreibung

We are Billie, the leading provider of Buy Now, Pay Later (BNPL) payment methods for businesses, offering B2B companies innovative digital payment services and modern checkout solutions. We are to create a new standard for business payments and have made it our mission to simplify the purchasing experience for all businesses making it a tool for growth. Our solutions are based on proprietary, machine-learning-supported risk models, fully digitized processes and a highly scalable tech platform. This makes us a deep-tech company building financial products, not the other way around. We love building simple and elegant solutions and we strive for automation and scalability.

As part of Decision Science, you will work on the models that decide, in milliseconds, whether a transaction is trustworthy. Your work directly shapes how much fraud we stop, how many good customers we approve, and how much risk the business carries.

About the role:

As a Fraud Data Scientist, you will be a core technical contributor within Billie's Decision Science group. You will design and build robust, scalable machine learning solutions that prevent fraud, with a direct and measurable impact on Billie's bottom line. You will own the end-to-end modeling lifecycle: defining the analytical approach, testing hypotheses, and deploying models that capture complex debtor behavior and emerging fraud patterns.

In more detail, you will:

Design and ship anti-fraud models, taking ownership of project priorities and delivering production-ready solutions.
Model debtor behavioral patterns, identify risk factors, and optimize the logic of Billie's real-time decision engine using quantitative analysis, data mining, and advanced ML.
Balance precision and recall under severe class imbalance, explicitly weighing the cost of false positives (customer friction) against missed fraud (financial loss).
Monitor deployed models for drift and adversarial adaptation, and retrain or recalibrate as fraud patterns shift.
Collaborate with data and software engineers, analysts, and product managers to improve decision logic, integrate new data sources, and extend system functionality.
Own the deployment and operationalization of ML services within real-time latency constraints, working with Engineering on infrastructure requirements such as containerization and event-driven architectures.
Share knowledge across the team and contribute to strong experimentation and coding practices.
Turn technical findings into clear, actionable recommendations through effective data storytelling for both technical and non-technical stakeholders.

What you bring to the team:

Must have:

3-5+ years in a quantitative or machine learning role, ideally in fintech or another high-transaction environment. Direct experience in fraud prevention or risk modeling is strongly preferred.
Proven advanced proficiency in Python (e.g. pandas, scikit-learn, xgboost) and SQL (Snowflake, Postgres, or MySQL).
Deep expertise in classification models (classical and deep learning), anomaly detection, and graph-based methods (e.g., graph neural networks, entity-link analysis).
Hands-on experience productionizing ML services, with a strong grasp of modern MLOps concepts such as containerization (Docker/Kubernetes) and event-driven architectures.
Proven ability to manage stakeholders across technical and non-technical functions, aligning technical roadmaps with business priorities.
Sharp problem-solving skills, with the ability to translate complex business challenges into clean, efficient, and scalable technical requirements.
Strong communication skills, with a track record of using data to influence strategy and drive cross-functional engagement.

Nice to have:

Experience with ML orchestration frameworks such as Metaflow, Apache Flink, or similar MLOps tooling.
Experience implementing LLM-based workflows (e.g., agentic pipelines, retrieval-augmented generation, or LLM-assisted feature extraction), particularly applied to fraud detection or risk signals.

What We Offer:

Challenging and impactful work that drives personal and professional growth
One of the best Virtual Shares Incentive Programs in the market, so that everyone at Billie is invested in our success
Flexible work hours and trust in your ability to deliver, empowering you to take control of your work-life balance
A hybrid working approach that allows you to work from home for up to 3 days per week
Enjoy 30 days vacation per year, sabbatical opportunities, and extra child sickness leave for parents.
Our “Catch a Ride with Billie” program that enables discounted access to Berlin Public Transport (BVG), Deutschland-Ticket, OR JobRad
A yearly development budget to broaden your skill set and horizons
Free German group classes
An English-speaking, multicultural team with more than 40 nationalities
Building meaningful connections with your colleagues through company and team events, interest groups, the Billie run club, game nights, and more, powered by our Formula Fun Team!

Billie is an equal opportunity employer and we do not discriminate on the basis of race, color, religion, sexual orientation, gender identity or expression, national origin, age, disability, or any other protected characteristic. We are committed to creating an inclusive environment where everyone feels they belong. All qualified applicants are welcome and we especially encourage you to apply, even if you don't check every box in the job description.

For information about data processing see our "Recruiting Privacy Policy and further information": https://www.billie.io/en/candidate-information

Jetzt bewerben

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