Company Overview

The organisation operates within the financial technology sector, using quantitative analysis, statistical modelling, and data-driven methods to support financial decision-making. Its technology and analytics environment focuses on understanding financial market behaviour, evaluating risk exposure, improving analytical accuracy, and developing reliable models for complex financial activities.

The Data & Quantitative Research department brings together analytical professionals, software engineers, financial specialists, and data practitioners to transform large volumes of structured and time-sensitive data into meaningful insights. The team values rigorous statistical methods, reproducible research, transparent model assumptions, and practical solutions that can be implemented within real financial systems.

This remote opportunity is intended for a quantitatively skilled professional who can combine mathematical reasoning with programming expertise and financial understanding. The successful candidate will contribute to the development of analytical frameworks that help identify patterns, assess uncertainty, measure exposure, and improve the quality of financial decisions.

Role Summary

The Senior Quantitative Analyst will design, develop, validate, and maintain quantitative models used for financial analysis, risk measurement, forecasting, and performance evaluation. The role requires a strong foundation in statistics, probability, numerical methods, and programming, together with the ability to interpret complex datasets and translate analytical results into actionable recommendations.

Working with data engineers, risk professionals, product teams, and other quantitative specialists, the analyst will investigate financial behaviour, evaluate model assumptions, and develop repeatable analytical processes. Responsibilities will include preparing high-quality datasets, conducting statistical tests, implementing model improvements, and documenting the limitations and intended uses of analytical outputs.

The position also requires an appreciation of model governance. Analytical findings must be supported by appropriate evidence, tested against realistic scenarios, and communicated in a manner that enables stakeholders to understand both the value and limitations of the results.

Key Responsibilities

• Develop and maintain quantitative models for financial risk assessment, market behaviour analysis, performance measurement, and forecasting.

• Apply statistical techniques, probability distributions, regression analysis, time-series modelling, and other appropriate quantitative methods to financial datasets.

• Analyse historical and current financial data to identify trends, correlations, volatility patterns, anomalies, and potential changes in market behaviour.

• Build reproducible analytical workflows using Python, SQL, and suitable statistical or numerical computing libraries.

• Design experiments and backtesting procedures to evaluate model performance across different periods, market conditions, and relevant data segments.

• Assess model robustness by examining parameter sensitivity, estimation error, data quality, assumptions, and the effects of unusual observations.

• Develop risk metrics and analytical indicators that support exposure assessment, scenario analysis, stress testing, and decision-making.

• Collaborate with data engineers to improve data extraction, transformation, validation, and storage processes used by quantitative models.

• Investigate inconsistencies in analytical outputs, determine whether they arise from data defects or model limitations, and implement appropriate corrective measures.

• Establish model evaluation criteria and performance monitoring processes to identify deterioration in predictive accuracy or analytical reliability.

• Conduct scenario analysis to understand how changes in financial variables may influence risk exposure, model outputs, or expected performance.

• Produce clear technical documentation describing model design, assumptions, input requirements, validation results, and known limitations.

• Present analytical findings to technical and non-technical stakeholders through reports, dashboards, presentations, and structured discussions.

• Work with risk and compliance colleagues to ensure that relevant analytical methods support internal governance and applicable operational requirements.

• Identify opportunities to improve existing models through alternative methodologies, improved feature selection, better data coverage, or more efficient computation.

Requirements and Qualifications

• Bachelor’s or master’s degree in Quantitative Finance, Mathematics, Statistics, Economics, Physics, Financial Engineering, Computer Science, or a related quantitative discipline.

• Substantial experience in quantitative analysis, financial modelling, statistical research, risk analytics, or a comparable analytical role.

• Strong understanding of probability theory, statistical inference, regression, time-series analysis, and numerical methods.

• Advanced practical proficiency in Python, including experience with libraries such as pandas, NumPy, SciPy, and scikit-learn where appropriate.

• Strong SQL skills and experience working with large, structured datasets from multiple sources.

• Experience developing, testing, and evaluating quantitative models using historical data and defined performance criteria.

• Understanding of financial concepts such as volatility, drawdown, correlation, market exposure, and risk-adjusted performance.

• Ability to distinguish correlation from causation and recognise common sources of statistical bias, overfitting, and misleading analytical results.

• Familiarity with Git, reproducible research practices, automated testing, and collaborative analytical development.

• Strong problem-solving skills and the ability to explain complex mathematical concepts in clear, practical language.

• High attention to detail, sound analytical judgement, and a disciplined approach to documenting assumptions and limitations.

• Experience with financial markets, trading analytics, banking risk, payments data, or other fintech applications is an advantage.

• Familiarity with cloud-based data environments, model monitoring, or advanced simulation techniques is beneficial.

Benefits

• Remote working arrangements within Estonia, subject to the organisation’s employment policies.

• Opportunity to work on challenging quantitative problems within financial technology and data-driven financial services.

• Collaboration with experienced professionals across quantitative research, engineering, risk, and data analytics.

• Opportunities to expand expertise in financial modelling, statistical methods, and analytical software development.

• Access to professional learning, relevant technical training, and knowledge-sharing activities.

• An environment that values analytical independence, reproducible research, and evidence-based decisions.

• The opportunity to influence model quality, analytical standards, and the reliability of financial insights used across the organisation.

Application Instructions

Interested candidates should submit an up-to-date CV outlining their quantitative education, modelling experience, programming skills, and relevant financial analytics projects. Applicants are encouraged to describe one or two projects demonstrating how they developed, tested, or improved a quantitative model, including the methodology used, the evaluation process, and the conclusions drawn.

Applications should be submitted through the relevant recruitment platform or official hiring channel associated with the vacancy. Shortlisted candidates may participate in technical interviews covering statistical reasoning, Python or SQL, model validation, and quantitative problem-solving.

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