Quantitative Finance Researcher Jobs

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 BlockTech BlockTech is a fast-paced algorithmic market maker at the frontier of cryptocurrency derivatives and spot markets, expanding into new markets. We employ algorithms to trade our own capital, 24/7, across some of the most data-rich venues in finance. For the Summer of 2027, we're looking for a Quantitative Research Intern to join our option pricing team in Amsterdam. The role This is an 8-10 week summer internship at our Amsterdam office, giving you a unique opportunity to work on an end-to-end project with a real and direct business impact. You’ll work in a small team and get the chance to understand how a trading firm actually works from the inside. You will: Participate in a customized training program that will give you an understanding of financial markets in general, the company and the project. Own a research project from design to deployment guided by experienced Quantitative Researchers. Understand how technology and research enable our trading, market data, and infrastructure systems Collaborate with fellow researchers and software engineers to bring your project to success Learn from the best, all while leveraging cutting-edge technology What we're looking for Currently in your (pen)ultimate year and enrolled in, or planning to pursue, at least a Master’s degree in a quantitative field at a top European university. Available full-time in Amsterdam, for a duration of 8 - 10 weeks over the summer of 2027, starting on the 5th of July Working knowledge of Python A critical mind that follows things until completion, high ownership, and clear communication in English What's in it for you? A summer internship in a relaxed and modern office in Amsterdam A dedicated mentor, a team of fellow interns and full-time quantitative researchers, an in-house training program, and exposure to how engineering, research, and trading actually work together The opportunity to work at the forefront of automated trading, in an environment that embraces AI to both ac

About BlockTech BlockTech is a fast-paced algorithmic market maker at the frontier of global cryptocurrency derivatives and spot markets, expanding into new markets. We employ algorithms to trade our own capital, 24/7, across some of the most data-rich venues in finance. Crypto is one of the few markets where a researcher can still meaningfully move the edge. The data is abundant, the microstructure is novel, and the feedback loop between a research idea and live PnL could not be shorter. For the Summer of 2027, we're looking for a Quantitative Research Intern to join our Delta One team in Singapore. The role This is a 12-week summer internship at our Singapore office, giving you a unique opportunity to work on an end-to-end project with a real and direct business impact. You'll work in a small team and get the chance to understand how a trading firm actually works from the inside. Delta One is where we trade linear products: (perpetual) futures and spot. These are fast-moving, data-rich markets and a good place to see a research idea meet reality quickly. You will: Participate in a customized training program that will give you an understanding of financial markets in general, the company, and the project. Own a research project from design to deployment, guided by experienced Quantitative Researchers. Work with linear products and the data around them Understand how technology and research enable our trading, market data, and infrastructure systems. Collaborate with fellow quantitative researchers and software engineers to bring your project to success. Learn from the best, using technology built for speed and precision. What we're looking for Currently in your (pen)ultimate year studying a quantitative field at a top university in Singapore Available full-time in Singapore for a duration of 12 weeks over the summer of 2027, starting on the 10th of May Working knowledge of Python Comfort with probability, statistics, and working with messy real-world data A critic

About BlockTech BlockTech is an algorithmic trading firm operating at the frontier of global crypto derivatives and spot markets. We trade 24/7 across some of the fastest-moving, most data-rich venues in finance. Crypto remains one of the few markets where a researcher can still meaningfully move the edge: abundant data, novel microstructure, and the shortest possible loop between a research idea and live PnL. We're looking for an experienced Quantitative Researcher to take ownership of that edge and push it further. The role This is a senior, hands-on research seat on our trading floor. You'll own a research agenda end-to-end from hypothesis, dataset construction, feature engineering, model training, backtesting, live deployment, monitoring, and iteration. You'll be trusted to set its direction. You'll work shoulder-to-shoulder with fellow researchers, traders and analysts, shape how we price and trade, and help raise the bar for research across the floor, including mentoring less experienced researchers and influencing the tools and standards the team relies on. You will: Own price-prediction, signal, execution, and anomaly-detection models across crypto derivatives and spot markets from idea to live PnL Shape our research, backtesting, and trading infrastructure together with engineers, so good ideas reach production quickly and safely Own models in production: monitor live performance, diagnose decay, and iterate on what you ship Set research direction alongside traders, deciding which trades are worth making and why Raise the research bar by mentoring colleagues, reviewing work, and setting standards for rigour What we're looking for 4+ years of hands-on quantitative research and/or applied ML experience, with a track record of models you've taken into production trading live A strong academic foundation in a quantitative discipline (mathematics, physics, statistics, computer science, ML/AI, or similar) Fluency in Python and the modern ML stack (PyTorch and/or

About BlockTech BlockTech is an algorithmic trading firm operating at the frontier of global crypto derivatives and spot markets. We trade 24/7 across some of the fastest-moving, most data-rich venues in finance. Crypto remains one of the few markets where a researcher can still meaningfully move the edge: abundant data, novel microstructure, and the shortest possible loop between a research idea and live PnL. We're looking for an experienced Quantitative Researcher to take ownership of that edge and push it further. The role This is a senior, hands-on research seat on our trading floor. You'll own a research agenda end-to-end from hypothesis, dataset construction, feature engineering, model training, backtesting, live deployment, monitoring, and iteration. You'll be trusted to set its direction. You'll work shoulder-to-shoulder with fellow researchers, traders and analysts, shape how we price and trade, and help raise the bar for research across the floor, including mentoring less experienced researchers and influencing the tools and standards the team relies on. What you'll do Own price-prediction, signal, execution, and anomaly-detection models across crypto derivatives and spot markets from idea to live PnL using state-of-the-art ML Shape our research, backtesting, and trading infrastructure together with engineers, so good ideas reach production quickly and safely Own models in production: monitor live performance, diagnose decay, and iterate on what you ship Set research direction alongside traders, deciding which trades are worth making and why Raise the research bar by mentoring colleagues, reviewing work, and setting standards for rigour What we're looking for 4+ years of hands-on quantitative research and/or applied ML experience, with a track record of models you've taken into production trading live A strong academic foundation in a quantitative discipline (mathematics, physics, statistics, computer science, ML/AI, or similar) Fluency in Python and the m

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

At Graviton, every strategy, model and edge begins not with instinct, but with someone asking a better question than everyone else. As a Quantitative Research Intern, you'll spend 10-12 weeks working on research that directly shapes how we understand markets and build trading strategies. You'll collaborate with researchers and technologists to investigate market behaviour, test hypotheses, build predictive models and solve problems where the answers aren't obvious. This isn't an internship where you observe from the sidelines. Outstanding work has the potential to shape production strategies and generate real, measurable returns. Ideal candidates are quantitatively sharp, practically minded problem solvers, programmers, mathematicians and scientists who are excited to solve the hardest problems in our field, across our offices in the US, Singapore, Amsterdam and London. What You'll Work On You'll work on real research problems, not simulated exercises, discovering predictive patterns from billions of market events, designing statistical models, and applying machine learning to open research problems. Your work would include: Discovering predictive patterns from billions of market events Designing statistical models that identify trading opportunities Applying probability, optimization and machine learning to open research problems Building AI-powered research agents that accelerate experimentation Developing infrastructure for rapid, large-scale research Designing experiments and backtests to evaluate hypotheses Translating research into production-ready trading signals Exploring new ML, AI and quant finance techniques to find the next source of alpha What You'll Learn Beyond the technical exposure, you'll learn how experienced researchers approach hard problems: breaking them down, questioning assumptions, and deciding based on evidence rather than intuition. You'll be paired with a mentor, reporting manager and buddy, with regular code reviews and feedback through

At Graviton, every strategy, model and edge begins not with instinct, but with someone asking a better question than everyone else. As a PhD Quantitative Research Intern, you'll spend 10-12 weeks pointing the research skills you've spent years building at markets. You'll collaborate with researchers and technologists to investigate market behaviour, test hypotheses, build predictive models and solve problems where the answers aren't obvious. This isn't an internship where you observe from the sidelines, or re-run someone else's experiment. Outstanding work has the potential to shape production strategies and generate real, measurable returns. Ideal candidates are researchers who want to see their methods meet live markets: mathematicians, physicists, statisticians and computer scientists who are excited to solve the hardest problems in our field, across our offices in the US, Singapore, Amsterdam and London. What You'll Work On You'll work on real research problems, not simulated exercises. Your work would include: Discovering predictive patterns from billions of market events Designing statistical models that identify trading opportunities Applying probability, optimization and machine learning to open research problems Running large-scale simulations and experiments on high-performance research infrastructure Designing experiments and backtests to evaluate hypotheses Building AI-powered research agents that accelerate experimentation Developing infrastructure for rapid, large-scale research Translating research into production-ready trading signals Exploring new ML, AI and quant finance techniques to find the next source of alpha What You'll Learn Beyond the technical exposure, you'll learn what changes when research meets live markets: why a result that would survive peer review can still fail as a trading signal, how costs and capacity constrain an idea that looks clean on paper, and how to tell a model that is right from one that is right for the wrong reason.

At Graviton, we don't believe the best trading strategies come from following established playbooks. They come from asking better questions. Why does a pattern exist? What is driving it? Is it real or just noise? Can it be explained, modelled and turned into an edge? As a Quantitative Researcher, you'll work on those questions every day. You'll investigate market behaviour, develop quantitative models, build and test hypotheses and turn insights into trading strategies, working alongside researchers and technologists in an environment where an idea on a whiteboard can reach live trading quickly. This isn't a role where the answers are in a textbook. The problems are open-ended, the data is enormous, and finding the answers is the job. Ideal candidates are rigorous thinkers who genuinely enjoy mathematics, programming and figuring out things that aren't obvious, based in our Singapore, London, Amsterdam or US offices. What You'll Work On You'll work across quantitative research and systematic trading, taking ideas through the full lifecycle: question, hypothesis, experiment, model, validation, strategy, production. Depending on your team and research interests, your work would include: Discovering predictive patterns and sources of alpha from billions of market events Formulating hypotheses about market behaviour and designing experiments to test them Applying probability, statistics, optimization and machine learning to build predictive models and quantitative signals for systematic trading Designing robust backtests and statistical tests to distinguish genuine signals from noise Working with large and complex datasets to uncover patterns that aren't immediately visible Investigating market microstructure and how markets behave at different timescales Evaluating strategy performance, identifying weaknesses and continuously refining models Developing research infrastructure and AI-powered tools and agents that make experimentation faster and analysis deeper Working c

Quantitative Researcher Every trading strategy begins with a question worth answering. At Graviton, we believe the best trading strategies don't come from following established playbooks. They come from asking better questions. Why does a pattern exist? What is driving it? Is it real or just noise? Can it be explained, modelled and turned into an edge? As a Quantitative Researcher , you'll work on these questions every day. You'll investigate market behaviour, develop quantitative models, build and test hypotheses and turn insights into trading strategies. You'll work alongside quantitative researchers and technologists in an environment where research moves quickly from an idea on a whiteboard to something that can influence live trading. The problems are open-ended. The data is enormous. The answers aren't in a textbook. You'll be expected to find them. What You'll Work On You'll work across different areas of quantitative research and systematic trading. Depending on your team and research interests, your work may include: Discovering predictive patterns and sources of alpha from billions of market events . Formulating hypotheses about market behaviour and designing experiments to test them. Applying probability, statistics, optimization and machine learning to complex research problems. Building predictive models and quantitative signals for systematic trading. Developing and improving research infrastructure that enables faster experimentation and deeper analysis. Working with large and complex datasets to uncover patterns that aren't immediately visible. Designing robust backtests and statistical tests to distinguish genuine signals from noise. Investigating market microstructure and understanding how markets behave at different timescales. Evaluating strategy performance, identifying weaknesses and continuously refining models. Working closely with technologists to translate research ideas into efficient, production-ready systems. Building AI-powered r

About BlockTech BlockTech is a fast-paced algorithmic trading firm at the frontier of global cryptocurrency derivatives and spot markets. We trade 24/7 across some of the most data-rich, fast-moving venues in finance, and we use that data to build smarter models, sharper signals, and more adaptive systems. Crypto is one of the few markets where a researcher can still meaningfully move the edge. The data is abundant, the microstructure is novel, and the feedback loop between a research idea and live PnL couldn’t be shorter. We’re growing fast, and we’re looking for a Quantitative Researcher with a strong machine learning toolkit to help us push that edge further. The role As a Quantitative Researcher on our trading floor, you’ll own ideas end-to-end from hypothesis and dataset construction through feature engineering, model training and backtesting, all the way to live deployment, monitoring, and iterative improvement. You’ll sit shoulder-to-shoulder with Quantitative Traders and Quantitative Analysts, and your work will directly drive how we price and trade. You will: Collaborate closely with traders to translate research insights into systematic trading strategies Design, develop and deploy models for price prediction, signal generation, execution, and anomaly detection across crypto derivatives and spot markets using state-of-the-art AI and ML techniques. Build robust trading, backtesting and research infrastructure alongside our engineers, so promising ideas can move into production quickly and safely Own models in production: monitoring live performance, diagnosing decay, and iterating on what you ship What we're looking for A strong academic background in a quantitative discipline (Mathematics, Physics, Statistics, Computer Science, Econometrics, ML/AI, or similar), typically a PhD or an MSc with strong research experience Fluency in Python and the modern ML stack (PyTorch and/or TensorFlow, scikit-learn, NumPy, pandas) A deep, intuitive grasp of overfitting, g

About BlockTech BlockTech is a fast-paced algorithmic trading firm at the frontier of global cryptocurrency derivatives and spot markets. We trade 24/7 across some of the most data-rich, fast-moving venues in finance, and we use that data to build smarter models, sharper signals, and more adaptive systems. Crypto is one of the few markets where a researcher can still meaningfully move the edge. The data is abundant, the microstructure is novel, and the feedback loop between a research idea and live PnL couldn’t be shorter. We’re growing fast, and we’re looking for a Quantitative Researcher with a strong machine learning toolkit to help us push that edge further. The role As a Quantitative Researcher on our trading floor, you’ll own ideas end-to-end from hypothesis and dataset construction through feature engineering, model training and backtesting, all the way to live deployment, monitoring, and iterative improvement. You’ll sit shoulder-to-shoulder with Quantitative Traders and Quantitative Analysts, and your work will directly drive how we price and trade. What you’ll work on Collaborating closely with traders to translate research insights into systematic trading strategies Designing, developing and deploying models for price prediction, signal generation, execution, and anomaly detection across crypto derivatives and spot markets using state-of-the-art AI and ML techniques. Building robust trading, backtesting and research infrastructure alongside our engineers, so promising ideas can move into production quickly and safely Owning models in production: monitoring live performance, diagnosing decay, and iterating on what you ship What we’re looking for A strong academic background in a quantitative discipline (Mathematics, Physics, Statistics, Computer Science, Econometrics, ML/AI, or similar), typically a PhD or an MSc with strong research experience Fluency in Python and the modern ML stack (PyTorch and/or TensorFlow, scikit-learn, NumPy, pandas) A deep, intuit

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

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 alpha research requires mastery of multiple domains. The best research requires original research ideas, good intuition, strong data analysis skills, and good research planning. Our research directly drives our investment decisions. This role is highly selective. We have a long history of training extraordinarily talented academic researchers to succeed in the investment services industry. Prior experience in the financial services industry is not required. RESPONSIBILITIES Provide research assistance to full-time researchers Assist with data collection and preliminary analysis Follow, digest, analyze and improve upon the latest academic research DESIRABLE CANDIDATES Ph.D. candidates in finance, economics, mathematics, statistics, physics, computer science, or other quantitative discipline. Programming in any of the following: R, Python, or C++. Experience with SQL. Solid research experience in the field of the candidate’s specialty. Strong analytical and quantitative skills. Detail-oriented. Demonstrated ability to learn and apply new methodologies to real life problems. Willingness to take ownership of his/her work. Ability to work both independently and collaboratively within a team. Strong desire to deliver high quality results in a timely fashion. Prior experience in the financial services industry is not required.

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 Our research center in Singapore is seeking an experienced researcher with a strong background in alpha research. In this highly selective role you will have access to abundant research resources and exciting opportunities to discover high quality alpha signals that directly drive our investment decisions. RESPONSIBILITIES Conduct original quantitative alpha signal research Manage all aspects of the research process, including data analysis, alpha signal discovery, backtesting, trading idea generation, alpha signal/portfolio analysis and the management of production code Evaluate new datasets for alpha potential Follow, digest, analyze and improve upon the latest academic research DESIRABLE CANDIDATES 2+ years of research experience in Equities. Ph.D. or M.S. in finance, accounting, economics, mathematics, statistics, physics, computer science, operations research, or another quantitative discipline. Programming in any of the following: R, Python, or C++. Experience with SQL. Demonstrated ability to learn and apply new methodologies to alpha generation. Ability to work both independently and collaboratively within a team. Strong desire to deliver high quality results in a timely fashion. Detail-oriented. Willingness to take ownership of his/her work.