Billie is looking for a (Senior) Data Scientist to serve as a key technical pillar for our Fraud Prevention efforts. In this role, you will be responsible for designing and building robust, scalable machine learning solutions aimed at preventing fraud, owning the end-to-end modeling lifecycle within the broader Decision Science group.
What You'll Do
- Design and execute high-impact anti-fraud solutions, taking full ownership of project priorities and ensuring the delivery of high-quality, production-ready models.
- Apply extensive expertise in quantitative analysis, data mining, and advanced ML to model debtor behavioral patterns, identify risk factors, and optimize Billie's real-time decision engine logic.
- Collaborate deeply within cross-functional teams of data and software engineers, analysts, and product managers to improve decision engine logic, integrate new data sources, and enhance system functionalities.
- Own the deployment and operationalization of ML services, working closely with Engineering to define requirements for robust infrastructure, including containerization and event-driven architectures.
- Act as a technical mentor to junior team members, fostering a culture of technical excellence, rigorous experimentation, and best-in-class coding standards.
- Maximize the impact of technical findings on critical business decisions through excellent data storytelling and clear, actionable recommendations for stakeholders.
What We're Looking For
- 3-5+ years of experience in a data-driven, quantitative, or machine learning role.
- Advanced proficiency in Python (pandas, scikit-learn, xgboost) and SQL (Snowflake, Postgres, or MySQL).
- Strong grasp of data visualization tools like Tableau.
- Deep technical expertise in general classification models (classical and deep learning), anomaly detection algorithms, and graph-based networks.
- Hands-on experience productionizing ML services, demonstrating a strong understanding of modern MLOps concepts such as containerization (Docker/Kubernetes) and event-driven architectures.
- Proven ability to manage stakeholders across both technical and non-technical functions, aligning technical roadmaps with business priorities.
- Sharp problem-solving capabilities 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 organizational strategy and drive cross-functional engagement.
Nice to Have
- Experience ideally within fintech or a high-transaction environment.
- Direct experience in fraud prevention or risk modeling.
- Experience with ML orchestration frameworks such as Metaflow, Apache Flink, or similar MLOps tooling.
Technical Stack
- Python, pandas, scikit-learn, xgboost
- SQL, Snowflake, Postgres, MySQL
- Tableau
- Docker, Kubernetes
- Metaflow, Apache Flink
Team & Environment
You will be part of the broader Decision Science group, collaborating with cross-functional teams of data and software engineers, analysts, and product managers.
Benefits & Compensation
- 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.
- 30 days vacation per year, sabbatical opportunities, and extra child sickness leave for parents.
- “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.
- Company and team events, interest groups, the Billie run club, game nights.
Work Mode
This role follows a hybrid working approach that allows you to work from home for up to 3 days per week.
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.
