SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Teya is a Series C fintech building a financial platform for local businesses across Europe. The company focuses on providing simple tools, thoughtful design, and human support to replace clunky legacy financial services.
You will join the Credit & Analytics team as a Data Scientist, working on credit risk and pricing models that underpin Teya's lending products. Your work will span the full model lifecycle: data exploration, feature engineering, model development, deployment, and performance monitoring. You'll support decisions that directly impact small businesses across multiple geographies.
Key responsibilities include:
- Building and testing components of credit risk and pricing models under guidance from senior data scientists
- Exploring internal and external data sources, cleaning datasets, and engineering features to improve model performance
- Conducting analysis of merchant behavior, repayment performance, and product performance to answer business questions
- Monitoring model performance, identifying drift, and investigating root causes
- Supporting deployment of models onto the ML Platform and integration into the credit decisioning system
- Documenting work clearly to ensure models and analysis are understandable and maintainable
- Collaborating closely with Product, Engineering, Data Engineering, and Credit Strategy teams
This is positioned as an excellent entry-to-mid-level opportunity for someone at the start of their data science career who wants to learn fast in a high-growth fintech environment. You'll work alongside senior data scientists and see firsthand how your models shape credit decisions and portfolio performance.
Requirements:
- 0–2 years' experience in data science, quantitative analysis, or a related field
- Degree in a quantitative field (Mathematics, Statistics, Physics, Engineering, Computer Science, or related discipline)
- Solid grounding in statistics and understanding of core machine learning techniques
- Working knowledge of Python and SQL, with experience handling and analyzing real-world datasets
- Ability to explain analytical work clearly to both technical and non-technical stakeholders
- Curious, pragmatic, and commercially minded, with desire to understand how models drive business outcomes
- Eagerness to learn, take on feedback, and grow within a collaborative team
- Exposure to credit risk modeling or financial services is a plus
