Quantitative Researcher

IMC•New York, NY

2d•$175,000 - $275,000•Hybrid

About The Position

IMC Americas, Inc. is hiring multiple Quantitative Researchers in Chicago, Illinois, and New York, New York. The role collaborates with traders, developers, and researchers to evaluate and improve quantitative models for proprietary trading strategies through statistical analysis, simulation, and empirical research. Researchers develop methods for analyzing market data and trading statistics, formulate trading hypotheses as mathematical, statistical, and machine-learning models, and build tools, simulations, visualizations, and backtesting frameworks to evaluate hypotheses before deployment. The work also involves diagnosing model behavior, improving forecasting, identifying algorithmic gaps, defining requirements for new trading algorithms, and managing research projects from problem definition through post-deployment review. The role assesses model readiness and prioritizes research according to potential trading impact, feasibility, risk, resource needs, and strategic value. It also includes mentoring 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, or 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, including handling data quality issues such as gaps and outliers.
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 methods to gather and analyze market data, trading statistics, and new data sources, using research findings as the 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 solve research problems in proprietary trading.
Identify gaps in existing trading algorithms and propose approaches to improve pricing, execution, risk management, or trading performance.
Define design requirements for quantitative trading algorithms, including intended 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 whether quantitative models or trading strategies are ready for production by evaluating statistical evidence, trading risk, operational constraints, and expected profitability.
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.