Quantitative Engineer

Conviction; San Francisco, Northern (CA, KY); Hybrid; USD 150,000 - 210,000; Full time

Job summary

Conviction is seeking a Quantitative Engineer to work with the founders on building trading and backtesting systems that power Conviction. You will develop quantitative strategies, handle large datasets, and turn ideas into production-ready implementations.

You will focus on execution, backtesting, and strategy development across multiple markets, leveraging Python and time-series analysis to drive research and trading performance.

Qualifications

• Strong Python and quantitative programming skills.
• Experience building or researching systematic trading strategies.
• Strong understanding of statistics, probability, and quantitative methods.
• Experience working with large financial or time-series datasets.
• Experience designing and evaluating backtests.
• Understanding of common sources of backtest error, including look-ahead bias, survivorship bias, overfitting, transaction costs, and data leakage.
• Understanding of financial markets, order types, market structure, and trade execution.
• Experience with data analysis libraries and quantitative research tooling.

Responsibilities

• Develop trade-generation and execution logic for low-latency systems.
• Build and improve backtesting systems for historical performance.
• Develop production-ready, multi-agent trading strategies.
• Research and evaluate systematic trading strategies across equities, options, futures, and other financial markets.
• Work with large historical and real-time financial datasets (prices, fundamentals, news, alternative data).

Skills

Python

Quantitative programming

Time-series analysis

Statistics

Data analysis libraries

Tools

Pandas

NumPy

Backtesting libraries

Job description

As a Quantitative Engineer, you will work directly with the founders to build the trading and backtesting systems that power Conviction. You’ll develop quantitative strategies, work with large financial datasets, and turn trading ideas that can be tested and deployed in production.

What You’ll Do

• Execution & Trading Systems – Develop trade-generation and execution logic, with a focus on low-latency systems
• Backtesting – Build and improve backtesting systems that measure historical strategy performance
• Strategy Development – Build multi-agent systems that translate trading ideas into deployed, production-ready strategies
• Quantitative Research – Research, develop, and evaluate systematic trading strategies across equities, options, futures, and other financial markets
• Market Data – Work with large historical and real-time financial datasets, including prices, fundamentals, news, and alternative data

Qualifications & Experience

• Strong Python and quantitative programming skills
• Experience building or researching systematic trading strategies
• Strong understanding of statistics, probability, and quantitative methods
• Experience working with large financial or time-series datasets
• Experience designing and evaluating backtests
• Understanding of common sources of backtest error, including look-ahead bias, survivorship bias, overfitting, transaction costs, and data leakage
• Understanding of financial markets, order types, market structure, and trade execution
• Experience with data analysis libraries and quantitative research tooling

Preferred Qualifications

We’re especially interested in people who have worked in quantitative finance, trading, machine learning, or financial markets. Previous experience at a hedge fund, proprietary trading firm, asset manager, fintech company, market data provider, or quantitative research group is a plus.