Company Overview

The organisation provides digital financial services supported by analytical tools and risk management processes designed to maintain secure and sustainable operations. Its approach combines financial data, statistical analysis and technology to improve risk visibility, support responsible product decisions and strengthen operational controls.

Role Summary

The Fintech Risk Analytics Specialist will use financial and behavioural data to identify emerging risks, evaluate portfolio performance and improve risk monitoring across digital financial products. The position focuses on developing analytical models, assessing risk indicators and producing insights that support credit, fraud, transaction and operational risk decisions.

Working with risk management, data engineering, compliance and product teams, the specialist will help ensure that analytical methods are reliable, measurable and appropriate for their intended business applications.

Key Responsibilities

• Analyse financial transactions, customer behaviour and portfolio data to identify emerging risk patterns.

• Develop and maintain risk indicators, monitoring dashboards and analytical reports.

• Conduct exploratory data analysis to investigate changes in risk exposure and customer activity.

• Build and evaluate statistical models for risk segmentation, anomaly detection or predictive analysis.

• Assess model performance using appropriate validation methods and clearly defined metrics.

• Collaborate with fraud, credit risk and compliance teams to improve risk identification and monitoring.

• Investigate data quality issues that may affect risk assessments or reporting accuracy.

• Document analytical assumptions, model limitations and recommendations for stakeholders.

• Support the ongoing monitoring of risk trends and the effectiveness of implemented controls.

Requirements and Qualifications

• Bachelor’s degree in Statistics, Mathematics, Economics, Data Science, Finance or a related field.

• At least three years of experience in risk analytics, quantitative analysis or financial data analysis.

• Strong SQL skills and practical experience with Python or R.

• Knowledge of statistical methods, data modelling and analytical validation techniques.

• Understanding of financial risk indicators, transaction behaviour and digital financial services.

• Experience with business intelligence tools and presenting analytical findings to stakeholders.

• Familiarity with fraud analytics, credit risk, anomaly detection or model monitoring is desirable.

• Strong critical thinking, documentation and problem-solving skills.

Benefits

• Remote working arrangement within Estonia.

• Competitive compensation aligned with qualifications and relevant experience.

• Opportunities to develop advanced analytical and risk modelling skills.

• Collaboration with multidisciplinary financial technology and risk teams.

• Access to professional development and continuous learning opportunities.

Application Instructions

Candidates should submit an up-to-date CV and a concise cover letter describing their risk analytics experience, technical capabilities and relevant project work. Applicants may include examples of analytical models, monitoring improvements or data-driven risk assessments, provided confidential information is excluded.

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