Quantitative Researcher

IMC•Chicago, NY

1d•Hybrid

About The Position

IMC Americas, Inc. is hiring Quantitative Researchers for multiple openings in Chicago, Illinois, and New York, New York. The role conducts quantitative research to evaluate and improve models used in IMC’s proprietary trading strategies. Researchers collaborate with traders, developers, and other researchers, using statistical analysis, simulation, empirical research, and financial mathematics to develop and assess trading approaches. The position involves analyzing market and trading data, developing mathematical, statistical, and machine-learning models, and building research tools, simulations, visualizations, and backtesting frameworks. Researchers investigate model behavior, improve forecasts, identify opportunities to enhance trading algorithms, and coordinate with developers to integrate validated research into trading systems. They also assess whether models are suitable for production, prioritize research initiatives, and mentor graduate quantitative researchers and interns. Partial telecommuting is permitted.

Requirements

Bachelor’s degree or foreign equivalent in Computer Science, Financial Mathematics, Financial Engineering, Computational Mathematics, or a closely related quantitative, mathematical, or computational field.
Two years of experience in the job offered, a quantitative, technical, and mathematical role, or a related position.
Two years of experience applying statistical inference, optimization, regression analysis, time-series analysis, algorithm design, and other analytical and quantitative techniques to solve complex problems.
Two years of experience designing and implementing mathematical models and algorithms, including optimization, signal-generation, and statistical learning methods.
Two years of experience analyzing large-scale, high-frequency financial time-series datasets, including market, order book, trade, and derived signal data, to identify patterns, evaluate hypotheses, and measure model performance; experience handling data quality issues such as gaps and outliers is required.
Two years of experience applying statistical inference, time-series analysis, and model-validation best practices to analyze data and evaluate quantitative research results.
Two years of experience applying financial theory, financial mathematics, and financial engineering concepts, including market microstructure, derivatives pricing, risk modeling, and portfolio management.
Two years of experience collaborating with traders and developers on quantitative research for a quantitative trading environment.
Two years of experience using Python libraries including Pandas, NumPy, SciPy, scikit-learn, statsmodels, PyTorch, XGBoost, LightGBM, Numba, Polars, Dask, and Matplotlib for quantitative analysis.
Two years of experience applying quantitative research methodologies for proprietary trading.
Two years of experience using numerical optimization techniques.
Two years of experience building options portfolios.
Two years of experience developing trading systems that trade at low-latency, millisecond granularity.

Responsibilities

Collaborate with traders, developers, and researchers to evaluate, validate, and improve quantitative models used in proprietary trading strategies through statistical analysis, simulation, and empirical research.
Design quantitative methods for gathering and analyzing market data, trading statistics, and new data sources, and use research findings as a basis for new trading strategies.
Build research-oriented computational tools, simulations, visualizations, and backtesting frameworks to evaluate quantitative hypotheses and measure model performance before production deployment.
Formulate trading hypotheses as mathematical, statistical, and machine-learning models; prototype algorithms; and coordinate with developers to integrate validated research into trading systems.
Apply statistical inference, optimization, numerical methods, and financial mathematics to diagnose model behavior, improve forecasting accuracy, and address complex research problems.
Identify gaps in existing trading algorithms and propose algorithmic approaches to improve pricing, execution, risk management, or trading performance.
Define design requirements for new quantitative trading algorithms, including market behavior, data inputs, model assumptions, implementation constraints, and expected trading impact.
Drive quantitative research projects from problem definition through hypothesis testing, model validation, implementation planning, and post-deployment performance review.
Assess statistical evidence, trading risk, operational constraints, and expected profitability to determine whether models or strategies are ready for production deployment.
Prioritize research initiatives based on expected PnL impact, technical feasibility, implementation risk, resource requirements, and strategic value to the trading desk.
Mentor graduate quantitative researchers and interns by reviewing research plans, challenging assumptions, evaluating analysis quality, and providing technical guidance.