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Teya is a Series C fintech building a financial platform for local businesses across Europe. The company provides simple tools, thoughtful design, and human support to help cafés, restaurants, salons, shops and entrepreneurs manage their finances with confidence.
You will lead and build the data science function within the Credit & Analytics team, owning the credit risk, pricing, and customer value models that underpin Teya's lending products. You will manage a team of data scientists, setting the modelling roadmap and standards while staying hands-on with the most complex and commercially important problems. Your responsibilities span the full model lifecycle: problem formulation, data exploration, deployment into the credit decisioning system, and ongoing performance management.
Key responsibilities include:
- Leading, mentoring, and developing a team of data scientists with clear goal-setting, regular 1:1s, and performance conversations
- Owning the hiring process for the data science team, defining profiles and raising the bar with each hire
- Translating credit and commercial priorities into a clear data science roadmap, prioritizing high-impact work
- Leading development of credit risk models, risk-based pricing engines, and customer lifetime value frameworks
- Shaping the credit decisioning framework, incorporating data, models, calculators and rules in partnership with Product, Engineering, Data Engineering and Credit Strategy
- Establishing best practices across the model lifecycle including documentation, validation, testing, monitoring and governance
- Identifying and assessing new data sources and modelling techniques for competitive advantage
- Presenting models and business impact to non-technical audiences, including leadership and the executive committee
You will work closely with Credit Strategy, Product, Finance, Engineering and Data Engineering teams. Your work will directly influence credit decisions, pricing and portfolio performance as Teya launches new lending products to small businesses across multiple geographies.
Requirements:
- 7+ years' experience in data science or quantitative modelling, with strong hands-on experience in credit risk modelling, ideally in SME lending within a fast-paced environment (fintech, scale-up, etc.)
- 2+ years' experience leading or managing data scientists, including hiring, coaching and developing team members
- Degree in a quantitative field such as Mathematics, Statistics, Physics, Engineering or related discipline (advanced degree a plus)
- Excellent applied quantitative skills, including machine learning techniques and sound understanding of statistical model validation
- Strong proficiency in Python and SQL, with experience deploying models into production alongside engineering teams
- Proven track record of leading models through the full lifecycle, from problem definition to deployment, monitoring and improvement
- Strong commercial judgement, connecting modelling work to credit performance, pricing and business strategy
- Excellent communication and stakeholder management skills, confidence to influence senior stakeholders across technical and non-technical teams
- Familiarity with lending economics a plus (expected loss, risk-adjusted returns, cohort repayment curves, NPV, customer lifetime value)
