Graviton Research Capital LLP Logo

Graviton Research Capital LLP

Quantitative Researcher - 2027 Graduates

Posted 3 Days Ago
In-Office
New York, NY, USA
Entry level
In-Office
New York, NY, USA
Entry level
Conduct quantitative research for systematic trading by analyzing market data, formulating and testing hypotheses, developing statistical and machine learning models, backtesting strategies, studying market microstructure, and partnering with technologists to deploy production-ready research and trading systems.
The summary above was generated by AI

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 closely with technologists to translate research ideas into efficient, production-ready systems
  • Exploring new techniques in machine learning, statistics and quantitative finance to uncover new sources of edge

No two research problems are the same. Good research finds patterns. Great research understands why they exist.

What Success Looks Like

Successful researchers at Graviton discover new sources of alpha and turn them into robust trading strategies, build models that hold up under real market conditions and not only in backtests, develop a deep understanding of market behaviour, create research frameworks and tools that make the broader team more effective, work with technologists to take ideas from research into production, and communicate complex quantitative ideas clearly and rigorously. Your impact isn't measured by how many models you build. It's measured by the quality of the ideas you discover and what those ideas enable.

Who We're Looking For

We're looking for exceptional problem solvers who genuinely enjoy mathematics, programming and figuring out things that aren't obvious.

  • Degree in a quantitative discipline such as mathematics, physics, computer science, statistics, operations research or machine learning
  • No active backlogs
  • Strong programming skills in Python and/or C++
  • Solid grounding in probability, statistics, linear algebra and optimization, with experience in machine learning, statistical modelling or data analysis, whether through coursework, research or personal projects
  • Comfort working with large, complex datasets, and strong analytical thinking
  • A passion for applying quantitative thinking and rigorous research to solve open-ended problems in markets
  • Excellent communication skills, with the ability to explain complex ideas clearly

Prior exposure to quantitative research, algorithmic trading, market microstructure, competitive mathematics or machine learning is valuable, but we care more about how you think than whether you've worked in finance before.

Our Culture

More than specific tools or techniques, we care how you think. We look for people who are curious enough to keep asking why, analytical enough to separate signal from noise, persistent enough to pursue difficult problems, intellectually honest enough to abandon an idea when the evidence says so, and humble enough to keep learning.

At Graviton, researchers sit alongside technologists on specialized trading teams that operate with significant autonomy. You'll have meaningful ownership of research problems from day one: forming your own hypotheses, challenging existing approaches and following the evidence wherever it leads, with the infrastructure and datasets to test ideas at scale, competitive compensation, and a culture where the best idea wins regardless of who proposed it.

Markets are among the most complex systems humans have ever created, and they never stop changing. Neither should our thinking. Every day, someone asks a better question and finds an answer nobody had. That someone could be you.

Similar Jobs

50 Minutes Ago
In-Office
Expert/Leader
Expert/Leader
Artificial Intelligence • Fintech • Payments • Business Intelligence • Financial Services • Generative AI
Lead the architecture and evolution of payment acceptance infrastructure. Design and operate scalable backend microservices, REST APIs, event-driven systems, and integrations for high-volume distributed payments. Drive initiatives across gateways, acquiring, issuing, card networks, and local payment methods. Partner cross-functionally, improve reliability and observability, address technical debt, establish strong engineering practices, and mentor engineers.
Top Skills: AWSCassandraDatadogDockerElkGCPHazelcastJavaKafkaKotlinKubernetesMicroservicesNew RelicOracle CoherencePrometheusReactRest ApisSpringSpring BootStatsd
51 Minutes Ago
In-Office
Senior level
Senior level
Artificial Intelligence • Fintech • Payments • Business Intelligence • Financial Services • Generative AI
Design, build, and operate backend microservices for global payments acceptance. Implement REST APIs, integrate with card networks and local payment methods, work with Kafka and distributed datastores, perform architectural reviews, address tech debt, and improve operational tooling and reliability across cloud and containerized environments.
Top Skills: Aws Ec2CassandraCloudfrontDatadogDockerElbElkGCPHazelcastIdealJavaJavaScriptKafkaKlarnaKotlinKubernetesMastercardNew RelicOracle CoherencePrometheusRdsReactRestSpringSpring BootStatsdTrustlyTypescriptVisa
3 Hours Ago
Easy Apply
Hybrid
Easy Apply
Senior level
Senior level
Fintech • Payments • Financial Services
Leads People data engineering infrastructure, building Workday and HR platform ETL/ELT pipelines, scalable data models, data marts, quality frameworks, lineage tracking, and governed data products. Ensures privacy, security, masking, and GDPR compliance while enabling People Analytics, workforce planning, executive reporting, and AI applications. Partners with analytics, engineering, applications, and security teams to automate reliable, reusable data workflows.
Top Skills: AirflowBigQueryCi/CdDagsterDbtGemini EnterpriseGitGreat ExpectationsPythonRaasRest ApisSnowflakeSoap ApisSQLWebhooksWorkdayWql

What you need to know about the NYC Tech Scene

As the undisputed financial capital of the world, New York City is an epicenter of startup funding activity. The city has a thriving fintech scene and is a major player in verticals ranging from AI to biotech, cybersecurity and digital media. It also has universities like NYU, Columbia and Cornell Tech attracting students and researchers from across the globe, providing the ecosystem with a constant influx of world-class talent. And its East Coast location and three international airports make it a perfect spot for European companies establishing a foothold in the United States.

Key Facts About NYC Tech

  • Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
  • Key Industries: Artificial intelligence, Fintech
  • Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
  • Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
  • Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account