Company Overview

The organisation uses financial data, analytical methods, and structured risk processes to evaluate credit exposure and support responsible lending or other credit-related business activities. Its risk function works to understand repayment behaviour, assess portfolio quality, identify emerging deterioration, and provide decision-makers with reliable information about potential credit losses.

The organisation values evidence-based risk assessment, consistent analytical methods, data integrity, and responsible decision-making. It seeks a Credit Risk Analyst with strong quantitative skills and an interest in developing analytical approaches that support portfolio monitoring, credit policy evaluation, and risk forecasting.

The successful candidate will work with risk managers, finance professionals, data specialists, and business stakeholders to analyse credit performance and improve the quality of risk reporting. The working environment encourages clear documentation, careful interpretation of model results, and an appropriate balance between risk assessment, business objectives, and fair treatment of customers.

Role Summary

The Remote Credit Risk Analyst will analyse credit-related data to evaluate portfolio performance, identify risk trends, and support the development and monitoring of credit risk models. The role involves examining repayment behaviour, delinquency patterns, default indicators, exposure concentrations, and other measures used to understand credit quality.

The successful candidate will use SQL, Python or comparable analytical tools, and statistical techniques to prepare datasets, investigate risk drivers, monitor portfolio indicators, and support predictive modelling. They will work with relevant stakeholders to assess changes in credit performance, evaluate analytical assumptions, and develop reporting that supports sound credit decisions.

The role also includes supporting model performance reviews, conducting portfolio segmentation, analysing potential losses, and identifying early indicators of deterioration. The successful candidate must be able to explain quantitative findings clearly, maintain strong data-quality standards, and recognise the limitations of predictive models when they are used to inform decisions affecting customers and the business.

Key Responsibilities

• Analyse credit portfolio data to identify trends in repayment behaviour, delinquency, defaults, exposure, and overall portfolio quality.
• Develop and maintain reports that monitor key credit risk indicators, including arrears rates, default rates, loss measures, exposure concentrations, and portfolio performance.
• Use SQL, Python, spreadsheets, and analytical tools to extract, clean, validate, and analyse credit-related datasets.
• Investigate changes in credit performance across customer segments, products, origination periods, exposure categories, and other relevant dimensions.
• Support the development and evaluation of predictive models used for credit risk assessment, default prediction, loss estimation, or portfolio monitoring.
• Assist with the preparation of modelling datasets, feature development, data validation, and analysis of relevant explanatory variables.
• Evaluate model performance using appropriate statistical and classification metrics, ensuring that results are interpreted in the context of the intended business use.
• Support the monitoring of model stability and identify changes in input data, customer characteristics, or portfolio conditions that may affect predictive performance.
• Conduct portfolio segmentation to identify differences in risk patterns and determine where additional analysis or monitoring may be required.
• Analyse historical repayment behaviour and delinquency migration to identify potential early-warning indicators of credit deterioration.
• Support scenario analysis and stress testing to assess how changes in economic conditions, customer behaviour, or portfolio composition could affect credit outcomes.
• Examine credit loss patterns and support the preparation of analytical inputs for expected-loss estimates or related risk reporting where applicable.
• Investigate discrepancies between source records, credit risk reports, model outputs, and finance data, coordinating corrections with relevant teams.
• Work with credit risk managers and business stakeholders to translate policy questions into structured analytical investigations.
• Evaluate the potential effect of changes to credit policies, underwriting criteria, limits, or portfolio composition using historical data and suitable analytical methods.
• Prepare clear analytical reports explaining observed trends, risk drivers, assumptions, limitations, and potential implications for portfolio management.
• Maintain documentation covering data sources, metric definitions, analytical methods, model assumptions, and reporting procedures.
• Support periodic model reviews by checking input quality, examining performance trends, and documenting areas requiring further validation.
• Collaborate with data engineering teams to improve the availability, consistency, and reliability of credit risk datasets.
• Help automate recurring credit risk reporting and develop reusable analytical workflows that reduce manual processing.
• Apply appropriate confidentiality, access-control, and data protection practices when handling financial records and personal customer information.
• Consider fairness, explainability, and appropriate governance when analysing credit models or other analytical tools that may influence customer outcomes.
• Support internal risk reviews and audit requests by preparing accurate supporting analysis and documenting relevant calculations.
• Identify opportunities to improve monitoring thresholds, portfolio segmentation, data validation, and analytical reporting processes.
• Keep informed about relevant developments in credit risk measurement, predictive analytics, financial modelling, and applicable risk governance practices.

Requirements and Qualifications

• Experience in credit risk analysis, risk analytics, portfolio analysis, financial modelling, or a related analytical role.
• Strong analytical skills and a sound understanding of statistics, probability, and quantitative data interpretation.
• Practical experience with SQL and at least one analytical programming language, such as Python or R.
• Understanding of credit risk concepts, including default, delinquency, exposure, repayment behaviour, and credit portfolio performance.
• Experience analysing structured financial datasets and preparing accurate recurring risk reports.
• Familiarity with predictive modelling, classification techniques, feature analysis, or model performance evaluation is advantageous.
• Ability to interpret key credit risk metrics and explain how changes in portfolio composition may affect reported outcomes.
• Understanding of data quality controls, missing-value treatment, outlier investigation, and analytical validation.
• Familiarity with credit scoring, probability of default, loss given default, exposure at default, or expected credit loss concepts is beneficial, depending on the organisation’s activities.
• Ability to conduct portfolio segmentation, trend analysis, sensitivity analysis, and scenario-based investigations.
• Strong attention to detail and a disciplined approach to checking calculations, validating source data, and documenting assumptions.
• Ability to communicate technical findings to credit risk managers, finance colleagues, and non-technical stakeholders.
• Familiarity with business intelligence tools, reporting automation, version control, or data visualisation is advantageous.
• Awareness of responsible data use, customer privacy, model governance, and fairness considerations in credit-related analytics.
• Strong organisational skills and the ability to manage recurring reporting deadlines alongside ad hoc analytical requests.
• A degree in finance, economics, statistics, mathematics, data science, business analytics, or a related field is advantageous; equivalent professional experience is also valued.
• Professional proficiency in English is required; knowledge of Estonian is an advantage.
• Ability to work independently and collaborate effectively with a distributed team.

Benefits

• Fully remote working arrangement within Estonia.
• Full-time, permanent employment in a credit risk and portfolio analytics function.
• Opportunities to work with financial datasets, predictive modelling, portfolio monitoring, and credit risk reporting.
• Collaboration with credit risk managers, data specialists, finance professionals, and business stakeholders.
• Opportunities to develop expertise in credit analytics, model monitoring, scenario analysis, and risk forecasting.
• Professional development through quantitative projects, analytical reviews, and cross-functional collaboration.
• A working environment that values data quality, responsible risk assessment, analytical transparency, and continuous improvement.
• Opportunities to contribute to better risk visibility, stronger monitoring practices, and more reliable analytical decision support.

Application Instructions

Applicants should submit an up-to-date CV outlining their credit risk experience, quantitative skills, and familiarity with financial or portfolio data. Highlight experience with SQL, Python or R, credit risk reporting, predictive modelling, portfolio analysis, or model monitoring.

Candidates may include a portfolio, research summary, or project description demonstrating how they investigated a credit risk problem, validated the data, evaluated the results, and communicated the findings. Please remove personal customer information, confidential financial records, proprietary models, and restricted employer materials from supporting examples.

Applications will be assessed on quantitative reasoning, understanding of credit risk, data analysis skills, reporting accuracy, and the ability to communicate risk findings responsibly. Please apply through the official application channel associated with the vacancy and provide only information relevant to your qualifications and experience.

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