Senior Data Scientist – Financial Modelling

At Qred, we're on a mission to power entrepreneurs and small businesses with fast, fair, and data-driven financial solutions. We're one of Sweden's fastest-growing fintech banks - profitable, licensed, and expanding across Europe. Our culture is entrepreneurial, collaborative, and built on trust and ownership. Technology is the heart of our business, driving everything from credit assessments to customer experience.

We're now looking for a Senior Data Scientist who's ready to take ownership of the next generation of our financial risk models - shaping the foundation of how we assess, forecast, and manage risk in a high-growth environment.

You'll be our subject-matter expert in quantitative risk modelling, leading the design, development, and deployment of IFRS 9 / Expected Credit Loss (ECL) and other key financial models. You'll collaborate with risk, finance, and engineering to turn advanced analytics into production-grade systems that directly impact Qred's financial health and strategic decisions.

Your responsibilities will include:

• IFRS 9 / ECL Model Ownership

• Lead the end-to-end lifecycle of IFRS 9 models (PD, LGD, EAD) - from design to deployment.

• Integrate macroeconomic forecasting and forward-looking scenarios into our ECL framework.

• Advanced Financial Modelling

• Develop fair value models.

• Build expected repayment and prepayment models to forecast cash flows and customer behavior.

• Model Validation & Governance

• Document, monitor, and defend model design and performance for internal validation and external audits.

• Establish a robust model monitoring and recalibration process.

• Data Strategy & Analysis

• Partner with data engineering to integrate new data sources (e.g. new product, transactional or alternative).

• Conduct deep-dive analyses to uncover key drivers of credit loss and performance - delivering insights to the CCO and CFO.

• Collaboration & Mentorship

• Work closely with Finance and Credit as the primary quantitative expert.

• Mentor and guide junior data scientists and analysts to raise the technical bar.

Experience & Expertise

• 5+ years of experience in quantitative data science or risk modelling within financial services, fintech, or a high-growth tech setting.

• Deep hands-on knowledge of IFRS 9 and ECL model development (PD, LGD, EAD).

• Practical experience with fair value and behaviour models.

Technical Skills

• Expert in Python (pandas, NumPy, scikit-learn, Statsmodels) and SQL.

• Proficient with ML techniques (e.g., Logistic Regression, Random Forests, Survival Analysis).

• Familiar with relevant tools and cloud environments (Git, AWS stack).

Education

• Degree in a quantitative field (Statistics, Economics, Finance, Computer Science, Physics, or related).

Soft Skills

• Strong communicator, able to explain complex models and financial impact to non-technical teams.

• Pragmatic, structured, and outcome-focused, you can navigate ambiguity and deliver value.

• Ownership mindset, you take initiative and follow through.

• Experience with survival analysis for behavioural or prepayment modelling.

• Exposure to short-term consumer or SME lending environments (e.g., BNPL, revolving credit, credit card).

• Familiarity with other relevant regulatory frameworks.

You're a curious, hands-on problem solver who thrives in a fast-paced, entrepreneurial environment. You balance analytical depth with real-world pragmatism and love turning complex ideas into working systems. You care about impact - and you want your models to power better decisions for real small businesses.

One last thing

This is a full-time, permanent position based in Stockholm (Drottninggatan 98). While we encourage a hybrid setup, we do expect you to be at least three days per week at the office. Don't wait to send in your application, we are reviewing applications on a rolling basis. While the start date is flexible, the right candidate can join us immediately.

Qred celebrates diversity and does not discriminate based on ethnicity, religion, national origin, gender, sexual orientation, age, disability status, or any other applicable characteristics protected by law.