Careers at ES27
Bring a better
question.
Interesting work begins with curiosity. Explore the disciplines behind ES27: quantitative research, financial markets, data science, and the systems that connect them.
Explore opportunitiesThese role briefs describe potential opportunities. They are draft descriptions, not active vacancies. Hiring status, location, employment terms, and application details have not yet been confirmed.
The perspective
Curiosity to ask. Discipline to test.
Clarity to explain what you found.
Explore the roles.
9 roles / draft opportunities
01Quantitative Researcher — DerivativesQuantitative Research
Investigate how volatility, market structure, and execution shape derivatives strategies. Turn a trading hypothesis into a reproducible study, with careful attention to the assumptions behind the result.
The work
- Research volatility surfaces, option pricing, and relative-value relationships.
- Design backtests that account for transaction costs, liquidity, and path-dependent risk.
- Document findings, failure modes, and the conditions in which a model loses relevance.
Relevant experience
- Strong grounding in probability, statistics, and derivatives mechanics.
- Python experience with numerical analysis and financial time series.
- Ability to communicate uncertainty and distinguish robust evidence from overfitting.
02Quantitative Researcher — Statistical StrategiesQuantitative Research
Explore repeatable relationships in financial markets through statistical research. Develop simple, testable hypotheses and evaluate whether an observed relationship remains useful outside the sample that produced it.
The work
- Investigate cross-sectional and time-series signals across liquid markets.
- Build reproducible experiments with explicit benchmarks and out-of-sample evaluation.
- Assess signal stability, turnover, capacity, and sensitivity to changing conditions.
Relevant experience
- Practical knowledge of statistical inference, econometrics, or applied mathematics.
- Experience working with imperfect datasets and preventing information leakage.
- Clear Python code and a habit of testing alternative explanations.
03Quantitative Trader — Systematic MarketsTrading
Connect quantitative ideas with the practical realities of trading. Focus on execution quality, strategy behavior, and the feedback between research findings and observed market outcomes.
The work
- Monitor systematic strategies and investigate deviations from expected behavior.
- Analyze fills, slippage, liquidity, and exposure around market events.
- Translate trading observations into clearly defined research and engineering questions.
Relevant experience
- Sound understanding of market mechanics and financial instruments.
- Comfort with data analysis, trade reconciliation, and systematic processes.
- Disciplined judgment under uncertainty and clear written communication.
04Data Scientist — Financial MarketsData Science & ML
Extract reliable information from complex market data. Build analytical methods that help explain strategy outcomes, recognize changing conditions, and separate meaningful patterns from noise.
The work
- Develop research datasets, exploratory analyses, and interpretable models.
- Study performance attribution, market regimes, and execution outcomes.
- Communicate findings through clear visualizations and reproducible notebooks.
Relevant experience
- Strong applied statistics and Python skills, including pandas and scientific libraries.
- SQL proficiency and experience validating data quality.
- Good judgment about sample size, confounding factors, and statistical uncertainty.
05Machine Learning Research EngineerData Science & ML
Evaluate where machine learning can improve market research and analytical workflows. Combine rigorous model evaluation with engineering that makes experiments repeatable and results easier to inspect.
The work
- Develop model pipelines for time-series, classification, and forecasting experiments.
- Compare candidate models against simple baselines using appropriate validation.
- Investigate feature leakage, drift, calibration, and model failure modes.
Relevant experience
- Experience with Python and a modern machine learning framework.
- Understanding of temporal validation and the limits of financial prediction.
- Ability to turn experimental code into maintainable research tooling.
06Quantitative Developer — PythonEngineering
Build the tools that connect market data, research, and decision-making. Create dependable analytical software that makes quantitative work easier to reproduce, review, and extend.
The work
- Develop backtesting, analytics, and research orchestration tools.
- Implement pricing and risk calculations with clear numerical assumptions.
- Improve reproducibility, testing, performance, and documentation.
Relevant experience
- Strong Python engineering skills and familiarity with numerical computing.
- Experience with APIs, version control, automated checks, and structured data.
- Interest in financial markets and the ability to reason about edge cases.
07Trading Systems EngineerEngineering
Design reliable software around market data and trading workflows. Emphasize visibility, controlled failure behavior, and consistent operation under real-world connection and data constraints.
The work
- Build resilient integrations with market data and trading interfaces.
- Develop monitoring, reconciliation, alerts, and clear operational controls.
- Investigate latency, disconnects, stale data, and recovery behavior.
Relevant experience
- Experience with Python, C++, Rust, or another production systems language.
- Understanding of asynchronous processing, networks, and observability.
- A careful approach to reliability, incident diagnosis, and system boundaries.
08Market Data EngineerEngineering
Make market information dependable and usable. Develop data pipelines that preserve provenance, handle revisions, and support research without silently introducing misleading observations.
The work
- Build ingestion and transformation pipelines for price, options, and reference data.
- Validate timestamps, corporate actions, symbols, and instrument mappings.
- Design reproducible datasets with explicit quality checks and version history.
Relevant experience
- Strong SQL and Python skills, with experience in data pipelines.
- Knowledge of storage formats, schemas, and data quality monitoring.
- Attention to detail and an interest in the structure of financial datasets.
09Quantitative Risk AnalystRisk
Study the risks that aggregate performance numbers can hide. Develop clear views of exposures, drawdowns, stress behavior, and concentration across systematic trading approaches.
The work
- Analyze strategy and portfolio exposures under historical and hypothetical scenarios.
- Investigate tail behavior, correlations, liquidity constraints, and drawdown paths.
- Build reporting that makes assumptions and limitations visible.
Relevant experience
- Strong probability, statistics, and financial risk foundations.
- Python or R experience with simulation, scenario analysis, and reporting.
- Ability to explain complex risks in plain language and challenge optimistic assumptions.
Application links open your email app with the role in the subject line. Attach your resume and any relevant work samples before sending. This website does not collect or store applications.
A shared starting point
Thoughtful work.
Open questions.
The strongest ideas improve when they are examined carefully. ES27 values clear reasoning, practical skills, and a willingness to revise an assumption when the evidence changes.