Senior Data Scientist – Financial Risk & Analytics

About The Role As a Senior Data Scientist – Financial Risk & Analytics, you will play a key role in strengthening a data-driven risk and analytics organization within a growing financial institution. You will work hands-on with advanced analytics, predictive modelling, and decision automation to drive the modernization of underwriting and risk management capabilities. The ambition is to leverage data, machine learning, and quantitative methods to simplify and optimize decision-making processes while maintaining strong risk management, regulatory compliance, and business performance. This is a strategically important role where your work will directly influence financial outcomes, customer experience, and future growth. Key Responsibilities

• Design and develop data-driven and automated decision-making solutions.
• Drive initiatives focused on digitalization, automation, and process optimization.
• Apply statistical, econometric, and machine learning techniques to improve predictive performance and business outcomes.
• Analyze large and complex datasets using Python, SQL, SAS, or similar analytical tools.
• Develop and enhance scoring models, predictive models, and quantitative analytics frameworks.
• Support business and risk functions with advanced analytics, insights, and decision support.
• Ensure compliance with relevant regulatory requirements and governance frameworks.
• Develop, maintain, and improve IFRS 9 models and related methodologies (PD, LGD, EAD).
• Produce actionable insights, reports, and recommendations for senior stakeholders and decision-making forums.
• Test, validate, and monitor analytical models, business rules, and system capabilities.
• Document methodologies, assumptions, and model performance to support governance, validation, and audit requirements.

Qualifications

• Several years of experience within Data Science, Risk Analytics, Quantitative Modelling, Credit Risk, or related analytical disciplines within banking, financial services, fintech, or insurance.
• Proven experience developing automated decision-making processes, predictive models, and data-driven analytical solutions.
• Strong understanding of financial risk management, regulatory frameworks, and model governance.
• Experience with scoring models, portfolio analytics, IFRS 9, model monitoring, and performance evaluation.
• Academic degree in Data Science, Statistics, Mathematics, Economics, Engineering, Computer Science, or another quantitative field.
• Strong programming skills in Python, SQL, SAS, or similar technologies.
• Experience working closely with business stakeholders, risk teams, technology teams, and senior management.
• Excellent communication skills with the ability to translate complex analytical findings into business value.

Personal Qualities

• Positive, collaborative, and solution-oriented mindset.
• Highly analytical with strong problem-solving capabilities.
• Curious and proactive, with a passion for leveraging data to drive business improvements.
• Self-driven and comfortable taking ownership from analysis through implementation.
• Strong ability to navigate a dynamic environment and drive initiatives forward.

What We Offer Our client offers a competitive compensation package together with an attractive benefits program and flexible working arrangements. You will join a highly data-driven environment where analytics, innovation, and continuous improvement are central to the organization's future strategy. This is an opportunity to work at the intersection of data science, analytics, business strategy, and financial risk while contributing to high-impact initiatives and long-term transformation. Interested? We look forward to receiving your application. Interviews will be conducted continuously, and the position may be filled before the application deadline. The recruitment process is handled confidentially by a specialized executive search and recruitment partner. Further information about the company and role will be provided during the recruitment process.