Company Overview

The organisation operates at the intersection of financial markets, quantitative analysis, and trading technology. Its trading-related activities rely on structured market data, analytical research, systematic methodologies, and technology-supported processes to evaluate market behaviour and improve trading decisions.

The quantitative trading function works with technical specialists, researchers, and risk professionals to assess trading strategies, evaluate model performance, and monitor the reliability of automated trading workflows. The organisation emphasises evidence-based analysis, robust testing, data quality, and responsible management of trading risk.

Role Summary

The Algorithmic Trading Analyst will analyse systematic trading strategies, examine market data, evaluate backtesting results, and support the monitoring of algorithmic trading performance. The role focuses on determining whether trading models behave as expected under different market conditions and identifying opportunities to improve their reliability and effectiveness.

The successful candidate will combine quantitative reasoning, programming skills, and an understanding of financial markets. Working with quantitative researchers, software engineers, and risk specialists, the analyst will help translate research findings into measurable performance insights while ensuring that strategy evaluation accounts for transaction costs, liquidity constraints, and the limitations of historical data.

Key Responsibilities

• Analyse historical and current market data to identify price patterns, volatility characteristics, liquidity changes, and other factors relevant to systematic trading strategies.

• Support the research, development, testing, and evaluation of rule-based and quantitative trading models.

• Conduct backtesting and performance analysis using appropriate datasets, statistical methods, and established research procedures.

• Evaluate key strategy metrics, including returns, drawdowns, volatility, turnover, win rate, risk-adjusted performance, and transaction costs.

• Examine the impact of spreads, slippage, market impact, and execution delays on simulated and observed trading performance.

• Identify potential data-quality issues, look-ahead bias, survivorship bias, overfitting, and other sources of misleading backtest results.

• Develop and maintain analytical scripts, reusable research tools, and automated reporting processes using Python or comparable analytical technologies.

• Collaborate with quantitative researchers and developers to review model assumptions, validate calculations, and investigate unexpected strategy behaviour.

• Monitor live or simulated algorithmic trading activity and investigate deviations from expected performance.

• Support the preparation of research documentation covering methodology, assumptions, datasets, results, limitations, and recommendations.

• Assist with parameter sensitivity analysis, stress testing, and evaluation of strategy performance across different market regimes.

• Contribute to the improvement of research workflows, data pipelines, and model-validation procedures.

• Communicate analytical findings to technical and non-technical stakeholders through clear reports, charts, and presentations.

• Follow internal controls governing access to trading systems, research datasets, strategy deployment, and performance information.

Requirements and Qualifications

• Bachelor’s or master’s degree in Mathematics, Statistics, Computer Science, Economics, Financial Engineering, Physics, or another quantitatively focused discipline.

• Relevant experience in quantitative analysis, algorithmic trading, financial data analysis, investment research, or a similar analytical role.

• Strong Python skills for data analysis, statistical modelling, research automation, and performance evaluation.

• Familiarity with analytical libraries such as pandas, NumPy, SciPy, or comparable tools.

• Understanding of financial markets, trading instruments, market microstructure, and the practical factors that influence order execution.

• Experience with backtesting frameworks, time-series analysis, statistical testing, or strategy-performance measurement.

• Ability to work with structured datasets and query or manipulate data using SQL or similar technologies.

• Knowledge of risk-adjusted performance measures, drawdown analysis, volatility, and portfolio or strategy exposure.

• Understanding of common backtesting limitations and methods for reducing data leakage and overfitting.

• Familiarity with version control, reproducible research practices, and collaborative development workflows.

• Strong analytical problem-solving skills and the ability to explain technical findings in clear, structured language.

• Careful documentation habits and a willingness to challenge results that lack sufficient statistical or practical support.

• Ability to work independently in a remote setting while coordinating research activities and deadlines with distributed teams.

Benefits

• Remote working opportunity within Estonia, subject to employment eligibility and organisational requirements.

• Exposure to quantitative trading research, algorithmic execution, and financial market data.

• Opportunities to strengthen programming, statistical analysis, and systematic strategy-evaluation skills.

• Collaboration with professionals across quantitative research, trading, engineering, and risk management.

• Access to relevant research tools and data resources needed for assigned responsibilities.

• Opportunities to contribute to the development and refinement of systematic trading methodologies.

• A professional environment that values technical accuracy, reproducibility, and continuous learning.

Application Instructions

Applicants should provide a CV describing their quantitative, programming, and financial markets experience. Relevant project summaries involving backtesting, time-series analysis, trading strategy evaluation, or financial data processing may be included where appropriate.

Candidates should submit their application through the recruiting organisation’s official application channel. Shortlisted applicants may be asked to discuss an analytical project, explain a backtesting methodology, or demonstrate how they would investigate a trading strategy that performs differently in live conditions than in historical testing.

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