Company Overview

The organisation operates in a financial environment where market data, quantitative research, and systematic analysis support the evaluation of financial opportunities and trading-related decisions. Its analytical work involves studying market behaviour, assessing historical patterns, testing hypotheses, and developing research methods that help explain how financial variables interact under different conditions.

The organisation values rigorous research, reproducible testing, robust data handling, and a realistic understanding of model uncertainty. It seeks a Quantitative Analyst who can combine statistical methods, programming skills, and financial market knowledge to investigate data-driven strategies and produce objective research findings.

The successful candidate will collaborate with quantitative researchers, trading specialists, risk professionals, and technology teams. The role emphasises evidence-based analysis rather than unsupported market predictions, with particular attention to data quality, realistic testing assumptions, and the limitations of historical performance.

Role Summary

The Remote Quantitative Analyst will conduct research on financial market behaviour, develop quantitative models, and evaluate systematic trading or market analysis strategies using historical and, where available, live data. The role includes building analytical datasets, testing hypotheses, conducting backtests, measuring performance, and assessing whether observed results remain credible under different assumptions and market conditions.

The successful candidate will use Python, SQL, statistical techniques, and relevant financial data tools to investigate market relationships and develop repeatable research workflows. They will assess the impact of transaction costs, slippage, liquidity constraints, parameter sensitivity, and other factors that can materially affect the practical usefulness of a strategy.

This position requires strong analytical discipline, attention to research methodology, and the ability to explain results without overstating their significance. The analyst will document findings, identify weaknesses in research assumptions, and collaborate with relevant teams to determine whether promising research warrants further testing or operational consideration.

Key Responsibilities

• Research financial market behaviour using historical price data, trading records, economic indicators, and other relevant datasets.
• Develop quantitative methods for investigating market trends, volatility patterns, correlations, pricing relationships, and potential systematic trading signals.
• Use Python and appropriate analytical libraries to collect, clean, transform, and analyse time-series and financial market data.
• Write SQL queries to retrieve structured data and prepare reliable datasets for quantitative research.
• Formulate research hypotheses and define clear evaluation criteria before testing potential market relationships or trading strategies.
• Develop and maintain backtesting frameworks to evaluate strategy behaviour across historical periods and different market conditions.
• Apply appropriate validation methods, including out-of-sample testing and time-aware validation where suitable, to reduce the risk of misleading conclusions.
• Evaluate performance using relevant measures such as returns, volatility, drawdowns, turnover, exposure, risk-adjusted performance, and consistency across test periods.
• Incorporate realistic assumptions for transaction costs, spreads, slippage, execution constraints, and liquidity when evaluating strategy performance.
• Investigate overfitting, look-ahead bias, survivorship bias, data leakage, and other methodological problems that can distort research outcomes.
• Conduct parameter sensitivity analysis to determine whether results depend excessively on specific model settings or narrow historical conditions.
• Compare alternative modelling approaches and benchmarks to understand whether a strategy adds value beyond simpler or more robust methods.
• Examine how strategies behave during different volatility regimes, market trends, liquidity conditions, and periods of financial stress.
• Work with trading and risk specialists to understand practical constraints, position limits, exposure considerations, and execution requirements.
• Collaborate with technology teams to improve data pipelines, research infrastructure, computational efficiency, and reproducibility.
• Build reusable research tools, scripts, and analytical components that reduce duplicated effort and support consistent experimentation.
• Validate the accuracy and completeness of market datasets, investigating missing observations, timestamp inconsistencies, corporate actions, and other relevant data issues.
• Prepare research reports and presentations explaining methodology, findings, limitations, performance drivers, and recommendations for further investigation.
• Maintain version-controlled research code, experiment records, dataset descriptions, and documentation of assumptions.
• Monitor the behaviour of approved quantitative models or strategies and investigate material differences between expected and observed outcomes.
• Support the development of analytical dashboards and monitoring tools that make research findings easier to review.
• Apply sound data governance and security practices when handling market data, trading records, and confidential financial information.
• Participate in research reviews and communicate uncertainty, alternative explanations, and potential failure conditions to relevant stakeholders.
• Recommend improvements to research methods, backtesting controls, data quality checks, and quantitative evaluation standards.
• Keep informed about developments in quantitative finance, statistical learning, market microstructure, and financial data analysis.

Requirements and Qualifications

• Experience in quantitative analysis, quantitative research, financial market analytics, systematic strategy research, or a related technical field.
• Strong programming skills in Python, including practical experience with libraries such as pandas, NumPy, SciPy, or comparable analytical tools.
• Strong statistical and mathematical foundations, including probability, regression, time-series analysis, and hypothesis testing.
• Working knowledge of financial markets, traded instruments, market data, or trading-related analytical concepts.
• Experience cleaning and analysing time-series datasets and developing reproducible quantitative research workflows.
• Understanding of backtesting methods, performance evaluation, and the limitations of historical simulation.
• Ability to identify and investigate common research biases, including overfitting, look-ahead bias, and data leakage.
• Familiarity with SQL, relational databases, and data preparation for analytical applications.
• Understanding of performance and risk measures, including returns, drawdowns, volatility, and risk-adjusted metrics.
• Ability to assess the effects of transaction costs, liquidity, execution assumptions, and parameter sensitivity on research results.
• Experience using Git, maintaining structured code, and documenting analytical experiments is advantageous.
• Familiarity with financial APIs, cloud computing, distributed processing, or quantitative research platforms is beneficial.
• Strong attention to detail and the ability to challenge results that appear unusually strong or inconsistent with underlying assumptions.
• Ability to present quantitative findings clearly and explain the limitations of analytical conclusions.
• A degree in mathematics, statistics, economics, computer science, physics, quantitative finance, or a related discipline is advantageous; equivalent practical experience is also valued.
• Professional proficiency in English is required; knowledge of Estonian is an advantage.
• Ability to work independently, manage research priorities, and collaborate effectively with a remote team.

Benefits

• Fully remote working arrangement within Estonia.
• Full-time, permanent employment focused on quantitative research and financial market analytics.
• Opportunities to work with financial time-series data, statistical modelling, backtesting, and systematic research.
• Collaboration with quantitative researchers, trading specialists, risk professionals, and software engineers.
• Opportunities to improve research infrastructure, analytical methods, and model evaluation practices.
• Professional development through research reviews, technical collaboration, and exposure to financial market methodologies.
• A working environment that values methodological discipline, reproducibility, critical thinking, and responsible interpretation of results.
• Opportunities to build expertise in quantitative finance, market behaviour analysis, and research automation.

Application Instructions

Applicants should submit an up-to-date CV outlining their quantitative research experience, programming capabilities, and understanding of financial markets. Highlight projects involving time-series analysis, statistical modelling, strategy backtesting, market data processing, or performance evaluation.

Candidates may provide a research portfolio, technical paper, code repository, or project summary explaining a quantitative problem, the methodology used, the validation approach, and the limitations identified. Do not disclose proprietary trading strategies, confidential datasets, restricted source code, or information belonging to previous employers.

Applications will be assessed on programming ability, statistical reasoning, research discipline, financial market understanding, and the ability to evaluate quantitative results critically. 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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