Quantitative Researcher (2027 Graduate)

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 research tools and agents that accelerate idea generation, experimentation and analysis. Exploring new techniques in machine learning, artificial intelligence, statistics and quantitative finance to uncover new sources of edge. No two research problems are the same. The questions change. The data changes. The assumptions change. That's what makes the work interesting. The Research Challenge Quantitative research isn't about finding an answer. It's about finding an answer you can trust. You'll constantly ask: Is this signal real or statistical noise? Why does this pattern exist? What assumptions are we making? How robust is the result? What happens when the market changes? Can we explain the behaviour we're seeing? How do we turn an interesting observation into a repeatable trading strategy? You'll work through these questions using data, mathematics, experimentation and rigorous reasoning. Good research finds patterns. Great research understands why they exist. From Hypothesis to Impact At Graviton, research doesn't end with a model that looks good in a backtest. You'll have the opportunity to take ideas through the entire research lifecycle: Question → Hypothesis → Experiment → Model → Validation → Strategy → Production You'll work with researchers and technologists to understand how an idea behaves in real markets and how it can be translated into a robust trading system. The strongest ideas can move all the way from research to live trading and measurable PnL impact. Ownership From Day One Graviton operates on the belief that great research comes from giving great researchers room to think. You'll have meaningful ownership over research problems while working closely with experienced researchers and technologists. You'll be expected to: Form your own hypotheses. Challenge existing approaches. Design rigorous experiments. Decide what is worth investigating. Follow evidence wherever it leads. Know when an idea isn't working and move on. Communicate your thinking clearly. Turn promising research into something others can build on. There is no prescribed formula for finding alpha. Your job is to discover one. What Success Looks Like Over time, successful quantitative researchers at Graviton: Discover new sources of alpha and turn them into robust trading strategies. Develop a deep understanding of market behaviour. Build models that perform not just in backtests, but under real-world conditions. Challenge assumptions and improve existing research. Develop research frameworks and tools that make the broader team more effective. Work closely with technologists to take ideas from research into production. Communicate complex quantitative ideas clearly and rigorously. Take ownership of research outcomes and continuously raise the quality of the work around them. 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. Education Degree in Mathematics, Computer Science, Electrical Engineering or another highly analytical field. CGPA of 8.5 or above . No active backlogs. Technical Foundation You should have: Strong programming skills in Python and/or C++ . Strong fundamentals in probability, statistics, linear algebra and optimization. Experience with machine learning, statistical modelling or data analysis through coursework, research or projects. Strong analytical and problem-solving ability. Comfort working with large datasets and ambiguous problems. 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. What We Value More than specific tools or technologies, we're interested in your ability to think. We look for people who are: Curious enough to keep asking why. Analytical enough to separate signal from noise. Rigorous enough to question their own conclusions. Persistent enough to pursue difficult problems. Intellectually honest enough to abandon an idea when the evidence says so. Creative enough to look beyond conventional approaches. Humble enough to learn continuously. Ambitious enough to search for better answers. Why Graviton? Research With Real Impact Work on research that can directly influence live trading strategies and measurable outcomes. Problems Worth Solving: Work on open-ended problems where there are no predefined answers and no textbook solutions. Exceptional People: Learn alongside some of the strongest quantitative researchers and engineers in the industry. Freedom to Think: Our specialized trading teams operate with significant autonomy, giving researchers the freedom to explore ideas and pursue promising directions. Technology at Scale: Access sophisticated research infrastructure, massive datasets and high-performance computing to test ideas at scale. Ideas Over Hierarchy: We believe the best idea should win — regardless of where it comes from or who proposed it. Rewards That Match Impact: Competitive compensation with performance-driven growth and the opportunity to build a long-term career at one of the world's most technically demanding quantitative trading firms. About Graviton Research Capital: Founded in 2014, Graviton Research Capital has grown from a small team of researchers and technologists into a global quantitative trading firm. Our success is built on exceptional people, rigorous research and world-class technology. Across specialized trading teams, quantitative researchers and engineers work together to solve some of the most challenging problems in financial markets. Each team operates with significant autonomy, exploring different strategies, markets and research approaches while sharing a common commitment to intellectual curiosity, innovation and continuous improvement. We believe that the next great trading idea can come from anywhere. Markets never stop changing. Neither should our thinking. Ask better questions. Find the signal. Build what comes next.