New Grad Full-Time Quantitative Researcher

About the Role

An NYC-based hedge fund is hiring a new graduate Quantitative Researcher for a full-time position. The role is a strong fit for recent graduates who enjoy tackling difficult problems with mathematics, statistics, coding, and evidence-based analysis.

You will operate at the intersection of financial markets, statistical modeling, and technology. In partnership with traders, developers, and fellow researchers, you will look for market patterns, build forecasting models, evaluate trading ideas, and help develop quantitative strategies.

This opportunity is designed for an analytical, inventive graduate who wants to apply disciplined research methods to live financial markets.

Responsibilities

• Investigate financial markets to uncover signals, structures, and inefficiencies through quantitative methods.
• Work with large datasets such as market data, alternative data sources, and time-series records.
• Create, validate, and improve statistical models, predictive indicators, and trading approaches.
• Build and assess backtests, simulations, and experiments to measure the quality of research ideas.
• Partner with traders and developers to convert research insights into tools and strategies that can be used in production.
• Track model behavior over time and help refine strategies based on performance.
• Use concepts from statistics, machine learning, optimization, probability, and econometrics to solve trading-focused problems.
• Communicate findings effectively to both technical teammates and non-technical stakeholders.
• Keep up with market dynamics, quantitative techniques, and new research relevant to trading and investing.

Requirements

• You should be a recent graduate or an upcoming graduate from a Bachelor's, Master's, PhD, or equivalent program.
• A background or strong proof of ability in a quantitative discipline such as Mathematics, Statistics, Computer Science, Engineering, Physics, Economics, Finance, Data Science, Operations Research, or a similar area is expected.
• You need a solid grasp of probability, statistics, linear algebra, optimization, or machine learning.
• Programming experience in Python, R, C++, Java, Julia, MATLAB, or another comparable language is required.
• You should have worked with data through coursework, research, internships, projects, or self-directed study.
• Strong analytical reasoning, problem-solving ability, and careful attention to detail are important.
• You must be able to explain complex ideas clearly and collaborate well across teams.
• Interest in financial markets, trading, investing, or data-driven decision-making is necessary.
• Helpful but not mandatory: research, internship, or project experience in statistical modeling, machine learning, time-series analysis, forecasting, optimization, or quantitative finance.
• Helpful but not mandatory: experience with Python data science tools such as pandas, NumPy, SciPy, scikit-learn, PyTorch, TensorFlow, or statsmodels.
• Helpful but not mandatory: familiarity with SQL, large-scale data processing, cloud platforms, or distributed systems.
• Helpful but not mandatory: exposure to financial products such as equities, futures, options, fixed income, FX, commodities, or digital assets.
• Helpful but not mandatory: experience with backtesting, simulation, portfolio construction, or risk modeling.
• Helpful but not mandatory: participation in math contests, programming contests, research publications, Kaggle, hackathons, trading competitions, poker, chess, or other analytical competitions.

Benefits

• Full-time opportunity specifically structured for new graduates.
• Training and mentorship from experienced quantitative researchers, traders, and engineers.
• Chance to contribute to research that directly informs live trading and investment decisions.
• Exposure to financial markets, strategy development, data science, and trading infrastructure.
• A collaborative, intellectually demanding setting where strong ideas and high-quality research matter.
• Early responsibility for meaningful research work.
• Competitive pay and benefits.