Quantitative Researcher - Equities

IMC is looking for experienced quantitative researchers to develop high to mid frequency delta one trading strategies and predictive models for Equities markets. If you're excited about helping to push the boundaries of what we can do with Machine Learning in trading, unlocking the significant edges we have in execution, and collaborating to become the best trading firm worldwide, this may be the role for you.
We have longstanding and significant edges across market access, global reach, Options understanding and low latency. The rapid growth we've already seen in Machine Learning has unlocked these edges, and some of the most interesting and impactful problems are now being tackled.
IMC competes and wins as a team, with open idea sharing and collaboration across disciplines, desks and offices.
Your Core Responsibilities:

• Conduct large-scale data analysis to generate statistically robust predictions of market behavior, which guide all trading decisions and drive high-impact improvements across global offices.
• Work as part of an established and expanding research team, collaborating closely with traders, software and hardware developers globally to enhance our models and drive measurable improvements in production performance.
• Contribute to defining the strategic direction of research and tooling initiatives.
• Leverage creativity and expertise to quickly produce high-quality, testable ideas.
• Apply a disciplined and systematic approach to ensure results are robust and thoroughly validated.
• Build a deep understanding of market dynamics and potential optimization areas, working closely with operational traders to implement improvements.

Your Skills and Experience:

• Graduate & Postgraduate studies from a leading University; majoring in Machine Learning, Statistics, or STEM related subjects.
• 3+ years' experience as a Quantitative Researcher, with specific experience in the high to mid-frequency delta one space, ideally within Equities markets.
• A proven track record of developing value-adding and orthogonal features across multiple families.
• Practical experience extracting value from a range of sources (both public feed and alternative data).
• Strong programming skills in at least one language (python preferred).
• Solid understanding of statistics.
• Significant practical experience with at least one mainstream ML approach is required, whilst familiarity with a variety of machine learning approaches and the risks of overfitting is preferred.
• Exceptional interpersonal and communication skills, combined with a proven capacity to collaborate across disciplines and work autonomously to achieve goals.