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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.

Responsibilities

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.

Requirements

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.
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.

Skills

Python
pandas
scikit-learn
xgboost
SQL
Snowflake
Postgres
MySQL
classification models
deep learning
anomaly detection
graph-based methods
graph neural networks
entity-link analysis
MLOps
containerization
Docker
Kubernetes
event-driven architectures
Metaflow
Apache Flink
LLM-based workflows
agentic pipelines
retrieval-augmented generation
LLM-assisted feature extraction

Location

Berlin

Work Type

Hybrid
Full-time

Experience Level

3-5+ years

Benefits

Challenging and impactful work that drives personal and professional growth
One of the best Virtual Shares Incentive Programs in the market
Flexible work hours and trust in your ability to deliver
Hybrid working approach (up to 3 days remote)
30 days vacation per year
Sabbatical opportunities
Extra child sickness leave for parents
Discounted access to Berlin Public Transport (BVG), Deutschland-Ticket, OR JobRad
Yearly development budget
Free German group classes
English-speaking, multicultural team
Company and team events, interest groups, run club, game nights

About the Company

Billie is 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.

Equal Opportunity

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.