Quantitative Researcher
IMC is hiring a Quantitative Researcher in Chicago and New York, United States. It pays $175k-$275k a year and Level rates it ; you can apply on IMC.
Level's read on this job
- Builds AI ●●●● is how Level rates this role: the job is building AI systems.
- The posting mentions PyTorch, scikit-learn and XGBoost among its tools and skills.
- IMC has 167 other open roles on Level.
- Posted in the last few days.
AI in this role
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;
- Applying quantitative research methodologies for use in quantitative proprietary trading;
- Using numerical optimization techniques;
- Building options portfolios; and
- Developing trading systems that trade on low latency scale at the milisecond granularity.
#LI-DNI
The Base Salary range for the role is included below. Base salary is only one component of total compensation; all full-time, permanent positions are eligible for a discretionary bonus and benefits, including paid leave and insurance. Please visit Benefits - US | IMC Trading for more comprehensive information.
Salary Range$175,000—$275,000 USDAbout Us
IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing force in markets since 1989, we provide liquidity across trading venues, delivering the best outcome in value and risk management to investors. Using our own technology and capital, we build proprietary systems and algorithms that operate across global markets. Our researchers, traders, and engineers work as a collective, combining rapid experimentation, advanced infrastructure, and real-time feedback to turn insight into execution and execution into advantage.
How we rate this
Quantitative Researcher at IMC rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- What's a project where you used PyTorch hands-on?
- Walk me through how you've used scikit-learn in your day-to-day work.
- What are the limits of XGBoost that you've run into, and how did you work around them?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: PyTorch, scikit-learn, and XGBoost. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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