Senior Credit Risk Analyst

5 hours ago


Riga, Rīga, Latvia TF Bank Full time €40,000 - €80,000 per year

We are seeking a talented Senior Credit Risk Analyst to join our growing credit risk analytics team. This role is specifically designed for professionals with minimum of 3 years of experience in the lending industry who are looking to develop their career in credit risk analytics and statistical modelling within the banking sector.

Please note: This position is for a Senior Credit Risk Analyst role focused on credit risk assessment and modelling, not financial analysis.

About the Role

As a Senior Credit Risk Analyst, you will contribute to the development and enhancement of our credit risk analytics capabilities. This is an excellent opportunity for someone with a first experience in unsecured lending to get wider exposure and larger remit (affordability and underwriting scorecards and credit strategy, pricing and credit risk management) whilst working with cutting-edge statistical models and data-driven insights.

You will have the opportunity to work in an pan-European team and get exposure to credit risk analytics across our international markets including Baltic countries, Germany, Austria and other European markets.

Key Responsibilities

  • Data Analysis & Insights: Collect, analyse, and interpret data from multiple sources including internal systems and Credit Bureaus to identify trends, patterns, and opportunities that inform credit policy, credit limit and pricing strategies
  • Model Development: Contribute to developing and maintaining statistical models, including scorecards and other credit risk assessment tools
  • Stakeholder Collaboration: Provide clear, actionable insights and recommendations to stakeholders across the business, supporting informed decision-making and business growth
  • Cross-functional Teamwork: Work collaboratively with colleagues in the decision sciences and analytics team, as well as other departments across different locations
  • Communication & Reporting: Present complex analytical findings to both technical and non-technical audiences through clear presentations, reports, and data visualisations

Essential Requirements

Education & Experience:

  • Bachelor's degree in Mathematics, Statistics, Economics, Physics, Computer Science, Engineering, or related quantitative discipline. A Master's degree is preferred.
  • Minimum of 3 years of experience in analytics, data science, data analysis, or lending/credit assessment within the financial services sector.
  • Previous experience working for an unsecured lender (bank, fintech, or similar credit provider).

Technical Skills:

  • Strong analytical and problem-solving abilities with excellent attention to detail
  • Proficiency in SQL for data extraction, manipulation, and analysis
  • Understanding of statistical analysis techniques and data modelling methodologies
  • Experience or strong interest in predictive modelling (logistic regression knowledge would be advantageous)

Professional Skills:

  • Excellent written and verbal communication skills in English
  • Ability to translate complex data into clear, actionable business insights
  • Strong presentation skills with experience communicating to diverse audiences
  • Proven ability to work effectively in fast-paced environments whilst managing multiple priorities

Desirable Requirements

  • Programming experience in Python or R
  • Experience with data visualisation tools
  • Understanding of machine learning techniques applied to credit risk

What We Offer

  • Competitive salary and benefits package
  • Opportunity to work with advanced analytics and statistical modelling techniques
  • Professional development and training opportunities
  • Collaborative, inclusive working environment
  • Flexible working arrangements
  • Career progression opportunities within our growing analytics function

Application Requirements

Please note: We are unable to provide visa sponsorship for this role. Candidates must have the right to work in the EU without requiring sponsorship.



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