Quantitative Researcher

JOB RESPONSIBILITIES A highly collaborative, fast-growing team, Point72 Internal Alpha Capture (IAC) is developing scalable quantitative equity trading signals that leverage rigorous research, state-of-the-art machine learning methods, a broad range of public and proprietary data sources, and unparalleled computing power. We are looking for exceptional students to join us as quantitative researcher interns for Fall 2026, Spring 2027 and Summer 2027. Our interns will work closely with our team, receive comprehensive, in-depth training, and help develop novel signals that may have real impact. You will gain exposure to a variety of activities, which may include: Conducting full pipeline signal research, from ideation, via implementation and backtesting, to eventual application. Adapting and extending existing results in the broad field of machine learning; conducting novel research as needed to potentially develop new signals that may enhance portfolio returns. Analyzing very large data sets to extract features useful for predictive models. Providing research assistance to the team. Helping improve the team’s research infrastructure. DESIRABLE CANDIDATES Masters or PhD candidates in machine learning, computer science, finance, mathematics, or other quantitative disciplines. Demonstrated ability to conduct independent research utilizing machine learning over large, possibly noisy, data sets. Strong analytical and quantitative skills, and detail-oriented mindset. Proficient in Python or C++, and Linux environment. Excellent written and verbal communication skills, willing to proactively engage other team members in helping to foster a highly collaborative, team-oriented research environment. Willing to take ownership of his/her work, working both independently and within a collaborative team. Prior experience in the financial services industry is not required. Commitment to the highest ethical standards. The annual base salary range is $120000.00-$180000.00&n

Role/Responsibilities Research and develop in-house trading strategies, used by both discretionary and quantitative traders. Conduct quantitative research on market microstructure, applying knowledge to improve trading algorithm and identify market anomalies. Develop and maintain predictive models to optimize trade execution and minimize transaction costs, utilizing both linear and non-linear techniques. Collaborate with cross-functional teams, including portfolio managers and researchers, to design and implement solutions for the trading strategies. Stay current and report on changes in market microstructure. Requirements Master's degree or higher in a quantitative field such as Finance, Mathematics, Statistics, Computer Science, or a related discipline. A minimum of 2 years of experience in quantitative research, building statistical models for intraday study. Solid understanding of market impact. Proficiency in programming languages such as Python and C++. Experience in AWS is preferred. Strong knowledge in macro products, including FX and bonds, is a plus. Commitment to the highest ethical standards. The annual base salary range is $125,000-200,000 (USD). Actual compensation offered to candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level among other things. Details about eligibility for bonus compensation (if applicable) will be finalized at the time of offer.

About Cubist Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources. Role Quantitative researcher to help build out a systematic macro (futures, FX, and vol) strategies. Core focus will be working on mid-frequency alpha strategies. Job Description Develop systematic trading models across FX, commodities, fixed income, and equity markets Alpha idea generation, backtesting, and implementation Assist in building, maintenance, and continual improvement of production and trading environments Evaluate new datasets for alpha potential Improve existing strategies and portfolio optimization Execution monitoring Be a core contributor to growing the investment process and research infrastructure of the team Desirable Candidates Masters or PhD in mathematics, statistics, physics or other quantitative discipline. PhD in statistics or machine learning is a plus Experience in quantitative trading, ideally in FX or futures Experience with alpha research, portfolio construction and optimization Experience building statistical/technical, fundamental, and data driven signals Experience synthesizing predictive signals for both cross-sectional and time-series models Strong experience with data exploration, dimension reduction, and feature engineering Thorough understanding of and comfort using a variety of regression techniques—including OLS, MLS, Ridge, Lasso, and Bayesian inference—as well as techniques for dealing with errors that can occur, such as auto-correlation and heteroskedasticity Experience managing and running risk is a strong plus Proficiency in Python using the machine learning stack—numpy, pandas, scikit-learn, etc. Creative mindset Strong time management abi

Why Work with Us Solve impactful problems. At Seven Research, our close-knit team unites brilliant minds passionate about pushing technological and scientific boundaries in the global markets. We provide state-of-the-art resources and cultivate intellectual freedom, enabling our people to develop groundbreaking solutions to the most complex challenges of our field. Our collaborative approach celebrates excellence from everyone, regardless of tenure. Your Role Our Quantitative Researchers are the intellectual architects of our trading strategies, responsible for developing sophisticated models that transform complex data sets into actionable insights. You will apply rigorous statistical and machine-learning methodologies to diverse financial datasets, develop innovative approaches to understanding risk and returns, and design strategy frameworks leveraging first principles from economics and other fields. What We’re Looking For We seek ambitious and talented problem-solvers with strong quantitative backgrounds. No financial knowledge is expected. Quantitative Researcher candidates should have demonstrated capability in conducting cutting-edge research and analysis. We value team members who communicate clearly, have strong critical analysis skills, and can exercise ownership over projects. If working on challenging quantitative problems in a close-knit setting excites you, we’d love to get to know you. The ideal candidate will possess most of the following: PhD, master’s, or bachelor’s degree in computer science, statistics, physics, or a related quantitative field Proven track record developing sophisticated predictive models Strong programming skills, including mastery of Python for data analysis Knowledge of statistical and machine learning modeling frameworks Strong problem-solving skills and research acumen Strong communication skills to articulate complex technical concepts Experience working with large data sets and computationally intensive projects Jo

About Cubist Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources. Role/Responsibilities: We are seeking a quantitative researcher for the Cubist Machine Learning Research group with experience in machine learning, especially recent deep learning and natural language processing technology. Researchers will use a rigorous scientific method to develop sophisticated trading models and shape our insights into how the markets will behave. Successful researchers manage all aspects of the research process including data ingestion and processing, data analysis, methodology selection, implementation and testing, prototyping, and performance evaluation. Researchers will be introduced to industry standard datasets, including understanding which data may be relevant to a certain model or financial problem; how to collect, parse, and clean the data; how to incorporate the data into innovative functional models; how to construct and develop features from raw data; and how to estimate effectiveness of such features. Researchers will also be provided with the opportunity to implement the full breadth of their knowledge and training to actively participate in all stages of research & development of financial models through use of machine learning. Based on experience from working with existing industry-standard models and algorithms, researchers will learn how to construct their own models in order to solve complex financial problems and enhance data prediction capabilities within the financial services industry. Requirements: PhD or PhD candidate in machine learning, computer science, statistics, or a related field Experience with sequential modeling and time

About the Team: A well-established quantitative portfolio management team at Point72 is looking for an experienced quantitative professional to develop and trade systematic macro strategies, with a focus on market microstructure. The candidate will be given the resources and support to drive the build out and expansion of the quantitative macro business. Role/Responsibilities: Perform rigorous and innovative research to develop systematic signals for global macro (futures, FX, etc.) markets, with a focus on market microstructure signals Perform feature engineering with order book tick data at intraday to daily horizons Perform feature combination using various modeling techniques ranging from linear to machine learning models Participate in the research pipeline end-to-end, including signal idea generation, data processing, modeling, strategy backtesting, and production implementation Help drive the growth of the investment process and research capabilities of the team Work in a team of highly qualified and motivated individuals with access to a cutting-edge research and trading infrastructure and clean datasets Assist in building, maintenance, and continual improvement of production and trading environments Requirements: MS or PhD in physics, engineering, statistics, applied math, quantitative finance, or other quantitative fields with a strong foundation in statistics 4+ years of experience in quantitative research, building statistical models for intraday to daily trading, as part of a successful proprietary trading team with a track record Knowledge of market microstructure for futures and/or FX Prior experience with tick data based feature generation, modelling, and monetization Demonstrated proficiency in Python, R, or C/C++. Familiarly with data science toolkits, such as scikit-learn, Pandas Collaborative mindset with strong independent research abilities Commitment to the highest ethical standards