IMC Americas, Inc. has multiple openings for Quantitative Researcher (REQ ID: 4528527101) in Chicago, IL and New York, NY.

Your Core Responsibilities

Collaborate with traders, developers, and researchers to evaluate, validate, and improve quantitative models used in IMC’s proprietary trading strategies through statistical analysis, simulation, and empirical research (10%).
Design quantitative methods to gather and analyze market data, trading statistics, or new data sources and use the result of that research as the basis for new trading strategies (10%).
Build research-oriented computational tools, simulations, visualizations, and backtesting frameworks to evaluate quantitative hypotheses and measure model performance before production deployment (10%).
Formulate trading hypotheses as mathematical, statistical, and machine-learning models; prototype algorithms and coordinate with developers to integrate validated research into trading systems (10%).
Apply statistical inference, optimization, numerical methods, and financial mathematics to diagnose model behavior, improve forecasting accuracy, and address complex research problems in proprietary trading (15%).
Identify gaps in existing trading algorithms and propose new algorithmic approaches to improve pricing, execution, risk management, or trading performance (10%).
Define design requirements for new quantitative trading algorithms, including the intended market behavior, data inputs, model assumptions, implementation constraints, and expected trading impact (10%).
Drive complex quantitative research projects from initial problem definition through hypothesis testing, model validation, implementation planning, and post-deployment performance review (10%).
Determine whether quantitative models or trading strategies are ready for production deployment by assessing statistical evidence, trading risk, operational constraints, and expected profitability (5%).
Prioritize quantitative research initiatives by weighing expected PnL impact, technical feasibility, implementation risk, resource requirements, and strategic value to the trading desk (5%).
Mentor graduate quantitative researchers and interns by reviewing research plans, challenging assumptions, evaluating analysis quality, and providing technical guidance on algorithm design, trading research, and model validation (5%).

Partial telecommuting permitted. Multiple positions available. Requisition ID: 4528527101.

Your Skills and Experience

This position requires a Bachelors degree, or foreign equivalent, in Computer Science, Financial Mathematics, Financial Engineering, Computational Mathematics, or a closely related quantitative, mathematical, or computational field, and two (2) years of experience in the job offered or a quantitative, technical, and mathematical role or a related position.

Must have two (2) years of experience with each of the following skills:

Applying statistical inference, optimization, regression analysis, time-series analysis, algorithm design, and other analytical and quantitative techniques to solve complex problems;
Designing and implementing mathematical models and algorithms, including optimization, signal-generation, and statistical learning methods;
Analyzing large-scale, high-frequency financial time-series datasets — including market data, order book data, trade data, and derived quantitative signals — to identify patterns, evaluate hypotheses, and measure model performance, with experience handling data quality issues such as gaps and outliers;
Applying statistical inference, time-series analysis, and model-validation best practices to analyze data and evaluate quantitative research results;
Applying concepts from financial theory, financial mathematics, and financial engineering, including market microstructure, derivatives pricing, risk modeling, and portfolio management;
Experience collaborating with traders and developers conducting quantitative research for use in a quantitative trading environment;
Using Python coding libraries, including Pandas, NumPy, SciPy, scikit-learn, statsmodels, PyTorch, XGBoost, LightGBM, Numba, Polars, Dask, Matplotlib to assist in quantitative analysis;