Quantitative Research Intern in New York

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 datasets into actionable insights. You will apply rigorous statistical and machine-learning methodologies to diverse financial datasets, develop innovative approaches to understand 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 Intellectual curiosity and passion to uncover the inner workings of the global markets 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 Exper

Point72 is seeking a Quantitative Developer to join its Portfolio Construction and Analysis (PCA) team within the CIO office. ROLE Point72 is seeking a Quantitative Developer to join its Portfolio Construction and Analysis (PCA) team within the CIO office. The PCA team plays a vital role in the Equity L/S business at the firm, collaborating with investment teams to improve net revenue and risk adjusted returns. The Quantitative Developer will build, refine, and maintain an efficient technology infrastructure to enable and support the team’s quantitative research and analytical efforts. The ideal candidate is a smart, self-driven, and creative problem solver with strong technical and quantitative skills. RESPONSIBILITIES Architect, build, refine, and increase automation and robustness of the research infrastructure, including data access, risk modeling, and backtesting components Collaborate with quant researchers to implement framework for automating and presenting analytics, reports, and monitors Collaborate with firm-wide technology teams to deliver analytics and tools to users across the firm DESIREABLE CANDIDATES Undergraduate, Masters, or PhD in Computer Science or other quantitative discipline 3+ years of professional experience developing infrastructure to support an investment team Strong programming skills in Python and SQL Solid background in math/statistics/finance and familiarity with quantitative research methodologies and machine learning algorithms Understanding of and experience with the data ingestion process Ability to contribute to the analytics, quant research, and development process Understanding of portfolio construction is a plus Knowledge of equities is a plus High-energy personality and the ability to manage multiple tasks and deadlines in a fast-paced environment High degree of drive – must be a self-starter Ability to recognize and seize opportunities Depth of skills enabling him/her to perform ad hoc projects and task

Please send CVs to [email protected] with “202 7 KEPL Application” in the subject line. 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. About Our Team: KEPL is a fast-growing team at Cubist Systematic Strategies. We specialize in trading medium-frequency statistical arbitrage strategies with high Sharpe. The team is made up of people from top universities and top tier trading and tech firms. We have an open and collaborative culture, and we value rigorous research and innovative technologies. We are actively expanding into new markets and assets classes. Role: We are looking for full-time quantitative research analysts and software developers to join our fast-growing team and contribute to multiple new initiatives that aim to expand our business. The candidate should have a passion for innovation to solve research and trading problems. In this team, the candidate will gain full-stack exposure and build expertise in multiple aspects of quantitative research and trading. The candidate will play an essential role for the team’s successful expansion. The candidate will also collaborate with other team members to innovate our research infrastructure which will take our research and trading capability to the next level. Responsibilities: Conduct quantitative research to support the team’s investment process. Build technologies that bolster research & trading productivity. Develop, maintain, and improve the production trading capacity. Expand the system to new markets and asset classes. Requirements: Master or PhD degree in math, physics, computer science, engineering, or other related discipline. 1-3 years of prof

JOB DESCRIPTION Point72 Asset Management is seeking a Quantitative Analyst to join its Portfolio Construction & Analytics Team (PCAT) in the Office of the CIO. PCAT’s mandate is to study all drivers of success for Long/Short Equities investment professionals using data, analytics, and models of investor behavior and the market. These analyses are shared with portfolio managers to improve their strategies and with senior management to better allocate capital across investment teams. The ideal candidate is a highly analytical and creative problem-solver who can conduct independent research, work effectively as part of a team, and effectively summarize and communicate findings. RESPONSIBILITES Conduct bottoms-up analysis on the firm’s portfolios to identify strengths and weaknesses in idea generation, trading, and construction Form top-down views on which strategies offer the best risk/reward for the firm Invent new analytics to quantify skill and frameworks to support trade-offs between different sources of skill and risk Quantify market drivers to support pre-trade risk taking and decision making Communicate key findings to the team, PMs, and Co-CIOs Learn to develop in and contribute back to shared code base, reports, and research tools DESIRABLE CANDIDATES 2 or more years of experience in a quantitative research, portfolio management, or risk management role dealing with equities or equities investments Undergraduate, MS or Ph.D. in Finance, Computer Science, Mathematics, Engineering, or Physics, or other quantitative discipline Experience with statistical models and essential methods of quantitative finance Some level of proficiency in quantitative programming (Python preferred) Highly analytical Good interpersonal skills Detail-oriented Self-starter Ability to work cooperatively in a team-oriented, fast-paced environment A commitment to the highest ethical standards and to act with professionalism and integrity Point72 is a leading global

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

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 Algorithmic Developers are researchers and engineers who transform complex quantitative concepts into high-performance trading strategies and systems that are efficient, stable, and scalable. You will be continuously challenged to think creatively while conducting rigorous analysis and improvements regarding market dynamics, strategy behavior, and system performance. If you are excited by the prospect of solving these sorts of problems in a collaborative, research-driven environment, we would love to get to know you. What We’re Looking For We seek talented engineers committed to the best algorithmic development practices. No financial knowledge is expected. You should have excellent programming and analytical skills and be excited to apply these talents within our state-of-the-art research and trading infrastructure. You should be motivated to take ownership over core projects and relentlessly drive decisive improvements. The ideal candidate will possess most of the following: PhD, master’s, or bachelor’s degree in computer science or a related field Strong background in data structures and algorithms, and mastery of C++ Applied knowledge of statistical analysis. Passion for solving complex quantitative problems through technology Strong communication skills to articulate complex technical concepts Job Compensation Range The base salary range for this role is $200,000 - $300,000. This does not include other forms of compensation and benefits that are part of Seven Research’s comprehensive package. We

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 Dynamically managing portfolio risk by evaluating historical and real-time strategy performance. Overseeing automated trade execution and monitoring transaction costs. Supervising a small team of researchers and developers on a daily basis. Designing, researching, and managing sophisticated investment strategies by creating and engineering advance quantitative financial computer modeling systems to aid in analysis and research. Performing research to acquire historical and production data sources needed to build investment models. Designing and developing quantitative mathematical algorithms to link the diverse data sets from various providers. Engineering investment models that will make the buy and sell recommendations for the portfolios using advanced quantitative mathematic statistics and investment theory to design and program strategies that explicitly forecast risk, return, and trading costs. Using quantitative models to value securities. Conducting ongoing, cutting-edge quantitative research and analysis to enhance existing strategies and to expand into new markets. Developing aspects of successful statistical models, focusing on forecasting and optimization. Expanding trading universe and volume and expanding to other exchanges and products. REQUIREMENTS Advance degree (Masters or Ph.D.) in a computational or analytical field. Minimum of 10 years’ experience developing, researching or implementing quantitative models for equities, futures and/or FX. Hands on experience with all aspects of the research process, including methodology section, data collection and analysis, testing,

ROLE/RESPONSIBILITES Perform rigorous and innovative research to discover systematic anomalies in equity market End-to-end development: alpha idea generation, data processing, strategy backtesting, optimization and production implementation Identify and evaluate new datasets for stock return predictions Maintain and improve the portfolio trading in production environment REQUIREMENTS MS or PhD in physics, engineering, statistics, applied math, quantitative finance or other quantitative fields with a strong foundation in statistics 1+ years of work experience in systematic alpha research in equities Experience developing short term alpha signals (intraday or a few days) is a plus Demonstrated proficiency in R or Python Strong command of foundations of applied statistics, linear algebra, and time series models Ability to quickly and efficiently scrub, format, and manipulate large, raw data sources Strong knowledge of financial markets Highly motivated, willing to take ownership of his/her work Collaborative mindset with strong independent research ability

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. About our Team: KEPL is a fast-growing team at Cubist Systematic Strategies. We are specialized in medium-frequency statistical arbitrage strategies with high Sharpe. The team is made up of people from top universities and top tier trading and tech firms, including: D.E. Shaw, Two Sigma, Citadel, Meta, Google, etc. We have an open and collaborative culture, and we value rigorous research and innovative technologies. Please send CVs to [email protected] with “2026 KEPL QD Application” in the subject line. Role: We are looking for a quantitative software developer to join our team and contribute to multiple initiatives that aim to expand our business. The candidate should be passionate about financial market, data and technology. In this team, the candidate will gain full-stack exposure and build expertise in multiple aspects of quantitative trading. Responsibilities: Improve data ETL pipeline and build tools to analyze new data efficiently. Build technologies to bolster research & trading efficiency. Expand to new markets and asset classes. Manage day-to-day operations in a fast-paced environment. Requirements Master/PhD degree in math, computer science, engineering, or other related fields. 1-3 years of professional experience in software development or data science/analytics. Strong combination of quantitative skills and programming skills. Proficiency in Python; knowledge of common data analytics tools (e.g., SQL, pandas) is a plus. Familiarity with the Linux environment. Excellent written and verbal communication skills. Willing to work in a fast-paced start-

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

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 in the intraday to mid frequency systematic macro space. The candidate will be given the resources and support to drive the build out and expansion of the quantitative macro business. Role: Perform rigorous and innovative research to develop systematic signals for global macro (futures, FX, etc.) markets Work with price-volume and alternative data at intraday to multiday (up to 2-3 weeks) horizons in the mid-frequency space Participate in the research pipeline end-to-end, including signal idea generation, data processing, modeling, strategy backtesting, and production implementation Work in a team of highly qualified and motivated individuals with access to a cutting-edge research and trading infrastructure and clean datasets Responsibilities: Develop systematic trading models across global futures (equity indices, commodities and fixed income) and/or FX markets Alpha idea generation, backtesting, and implementation Evaluate new datasets for alpha potential Contribute to and enhance portfolio optimization, allocation and risk management processes Help drive the growth of the investment process and research capabilities of the team 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 signal research or portfolio management experience in futures markets and/or FX as part of a successful proprietary trading team with a track record Prior professional experience with signal combination, portfolio optimization and risk management 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 Commit

About Cubist: Cubist Systematic Strategies is one of the world’s premier investment firms. The firm 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. About our Team: KEPL is a fast-growing team at Cubist Systematic Strategies. We are specialized in medium-frequency statistical arbitrage strategies with high Sharpe. The team is made up of people from top universities and top tier trading and tech firms, including: D.E. Shaw, Two Sigma, Citadel, Meta, Google, etc. We have an open and collaborative culture, and we value rigorous research and innovative technologies. Please send CVs to [email protected] with “2026 KEPL DE Application” in the subject line. Role: We are looking for a quantitative software developer to join our team and contribute to multiple initiatives that aim to expand our business. The candidate should be passionate about financial market, data and technology. In this team, the candidate will gain full-stack exposure and build expertise in multiple aspects of quantitative trading. Responsibilities: Improve data ETL pipeline and build tools to analyze new data efficiently. Build technologies to bolster research & trading efficiency. Expand to new markets and asset classes. Manage day-to-day operations in a fast-paced environment. Requirements Master/PhD degree in math, computer science, engineering, or other related fields. 1-3 years of professional experience in software development or data science/analytics. Strong combination of quantitative skills and programming skills. Proficiency in Python; knowledge of common data analytics tools (e.g., SQL, pandas) is a plus. Familiarity with the Linux environment. Excellent written and verbal communication skills. Willin

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the team At its core, Stripe is a treasury company, and the Treasury Finance team (TFIN) is key to building the Global Payments and Treasury Network (GPTN). Within TFIN, the FX team collaborates closely with product, engineering, sales, and finance teams to manage Stripe’s Corporate and User related FX risk. This includes creating pricing models to keep pace with product expansion and developing hedging strategies to mitigate the risks to Stripe’s profitability. What you’ll do Stripe supports global commerce across 135+ currencies including digital assets.As a result, Stripe needs to build and maintain platform systems capable of pricing, estimating and hedging the resulting FX exposures. Grounded in an understanding of FX market conventions and dynamics, you will partner with Engineering, Data Science, Product, and Finance to maintain operational rigor today while shaping long-term pricing and hedging strategies. Responsibilities Monitor FX risks, execute trades , reconcile position, and attribute costs Build automated risk monitors, alerting tools, and analytical models using SQL and quantitative coding to enable observability and minimize operational toil Collaborate with Product and Engineering to guide the design and scaling of core FX infrastructure, trade execution flows, and liquidity provider integrations Develop risk-based FX pricing methodologies across products and currency pairs, factoring in volatility, transaction costs

As Point72 reimagines the future of investing, our Technology team is constantly evolving our firm’s IT infrastructure and engineering capabilities, positioning us at the forefront of a rapidly evolving technology landscape. We’re a team of experts who experiment and work to discover new ways to harness open-source solutions, modern cloud architectures, and sophisticated Artificial Intelligence (AI) solutions, while embracing enterprise agile methodologies. Our commitment to building and innovating in the AI space provides the framework intended to drive smarter decision making and enhance how we build and operate our platforms and applications. As a member of Point72’s Technology team, we encourage and support your professional development from day one—helping you advance your technical skills, contribute innovative ideas, and satisfy your own intellectual curiosity—all while delivering real business impact for our multi-billion-dollar global business. WHAT YOU’LL DO • Lead the design, development, and evolution of a production C++ analytics library used across the our macro business • Build and maintain pricing, calibration, curve construction, scenario, valuation, and risk analytics for macro products and related derivatives • Extend the existing C++ analytics library and deliver major new components that improve architecture, performance, consistency, and long-term extensibility • Translate quantitative models and analytical requirements into robust, high-performance, and maintainable C++ library components • Partner with quantitative researchers, traders, risk users, and technology partners to understand requirements and deliver robust C++ library capabilities • Provide technical judgment on library design, architecture, performance, interface design, and maintainability • Contribute to releases, production support, and performance improvements for production analytics •