Compensation: Depends on experience, location, and employment format: 1,500.00 - 2,000.00 USD / Monthly About the Role We are building a quantitative research platform for systematic investing across general financial markets. Our goal is to discover persistent statistical edges using data, mathematics, software engineering, and rigorous scientific methods. We develop our own research infrastructure, including market data pipelines, backtesting systems, factor research tools, portfolio construction methods, monitoring systems, and live trading integrations.

This is not a conventional software engineering or data science role. You will spend most of your time formulating hypotheses, designing experiments, analysing results, challenging assumptions, and determining whether an investment idea survives rigorous statistical testing. Many ideas will fail.

Understanding exactly why they fail is an essential part of the work. We optimise for scientific rigour, reproducibility, and quality of reasoning—not for shipping features as quickly as possible. What You’ll Do

Research quantitative investment signals and alpha factors.

Formulate hypotheses about market behaviour.

Design experiments to test those hypotheses.

Analyse large historical financial datasets.

Build reproducible research workflows in Python.

Evaluate predictive signals using statistical methods.

Calculate and interpret metrics such as information coefficient, turnover, drawdown, volatility, and risk-adjusted return.

Build and validate backtests.

Detect and eliminate look-ahead bias, survivorship bias, selection bias, overfitting, and data leakage.

Perform robustness, sensitivity, and out-of-sample testing.

Reproduce and extend ideas from academic papers.

Investigate why promising strategies stop working.

Improve data pipelines, research infrastructure, and automation.

Help move successful research ideas from notebooks into production.

Work directly with the founder on research priorities, platform architecture, and investment strategy development. Every week, you will formulate hypotheses, test ideas, analyse evidence, and decide whether a potential investment strategy deserves further development. If you have an interesting and defensible hypothesis, you will have the freedom to test it. What You’ll Learn You will gain practical experience in:

Quantitative investment research.

Statistical factor discovery.

Cross-sectional and time-series analysis.

Professional backtesting methodologies.

Portfolio construction and optimisation.





Risk management.

Financial data engineering.

Market data quality and bias detection.

Scientific research workflows.

Reproducible experimentation.

Systematic investing from initial idea to live execution.

Monitoring whether a strategy continues to behave as expected after deployment. You will learn not only how to find strategies that appear to work, but how to determine whether the evidence supporting them is real. Whom We’re Looking For You do not need previous experience in finance or quantitative trading.

We are more interested in your ability to think rigorously, work independently, write reliable code, and reason carefully about uncertain results. The strongest candidates often come from backgrounds such as:

Physics

Mathematics

Applied Mathematics

Computer Science

Electrical Engineering

Mechanical Engineering

Aerospace Engineering

Machine Learning

Computational Biology

Robotics

Signal Processing

Scientific Computing

Statistics or Econometrics You may be a recent graduate, a master’s student, a PhD candidate, a research assistant, or an early-career engineer.

Required

Qualifications

Strong Python programming skills.

Good understanding of probability and statistics.

Working knowledge of linear algebra.

Experience using NumPy and Pandas or similar analytical libraries.

Ability to work with complex and imperfect datasets.

Ability to explain your reasoning clearly.

Intellectual curiosity and willingness to learn independently.

English proficiency sufficient to read technical documentation and academic papers.

Strong attention to detail.

Comfort in discovering that your initial hypothesis was wrong Nice to Have

Experience with financial or time-series data.

Experience with machine learning.

Familiarity with hypothesis testing and experimental design.

Experience with Git and collaborative software development.

Knowledge of SQL.

Experience with Kaggle, academic research, programming competitions, or technical side projects.

Familiarity with portfolio optimisation or factor investing.

Experience reproducing results from research papers.





Knowledge of distributed computing or performance optimisation.

Experience with brokerage or market-data APIs.

Technology

Stack

Our current and planned research stack includes:

Python

NumPy

Pandas

Polars

DuckDB

Parquet

Git

Jupyter

VS Code vectorbt

Zipline

IBKR API

SQL You do not need experience with every technology listed above. Strong fundamentals and the ability to learn quickly matter more than familiarity with a specific framework.

This Role May

Be a Strong Fit If You

Enjoy solving open-ended analytical problems.

Like finding structure in noisy data.

Prefer evidence over intuition.

Are comfortable working on problems without an obvious solution.

Enjoy reading technical papers and reproducing their results.

Care about clean, reproducible research.

Have built technically challenging personal or academic projects.

Have participated in mathematical, physics, programming, or data-science competitions.

Enjoy debugging assumptions as much as debugging code.

Want to understand why a model works rather than simply calling a library function.

What You Do Not

Need

You do not need

Previous employment at a hedge fund.

Professional trading experience.

A finance degree.

CFA, MBA, or similar credentials.

Wall Street experience.

Detailed knowledge of financial markets before joining. A strong physicist, mathematician, scientist, or engineer can learn finance. Rigorous reasoning and technical depth are much harder to teach.

Interview

Process

Our selection process is designed to be practical and transparent:

Resume and project review.

A 30-minute introductory conversation.

A short take-home research exercise.

A technical discussion of your methodology, code, and conclusions.

Final conversation about the role, collaboration format, and expectations. The research exercise is based on a realistic quantitative problem. We evaluate the quality of your reasoning, experimental design, code, and communication—not whether you discover a profitable trading strategy. How to Apply

Please send

Your resume.

Your GitHub profile, if available.

A short description of the most technically challenging problem you have solved.

Links to relevant projects, publications, research, competitions, or technical work.

A brief explanation of why quantitative research interests you. We are especially interested in seeing evidence of how you approach difficult problems, even if your previous work was unrelated to finance.

📌 Junior Quantitative Analyst / Researcher (Argentina)
🏢 inbybob_
📍 Argentina