Are you ready to make an impact in the world of equities trading? As a Quantitative Researcher, you will drive innovation and optimize trading processes through advanced data analytics and modeling. Join our global team and leverage your skills to shape the future of financial markets.
As a Quantitative Researcher- Alpha Quant in the Cash Equities team, you will focus on data processing, signal feature construction, signal research, and systematic trading. You will help to drive the alpha research agenda for central risk book trading, using data analytics and programming to drive business transformation. Your role will involve feature engineering from various data sources, building robust alpha calibration and attribution framework, portfolio optimization, develop prediction models, collaborating with trading desks and implementing trading strategies.
We offer comprehensive training and growth opportunities to enhance your skills and advance your career. Our diverse team supports a wide range of business functions, providing a unique environment for professional development. We are committed to accommodating diverse needs and fostering an inclusive workplace.
Job Responsibilities:
Work closely with trading to build end-to-end design and implementation of daily and intraday signal research infrastructure, with special focus on central risk trading. Contribute from idea generation to production implementation: perform research, design prototype, implement signals, alpha calibration, and trading strategies, support their daily usage, and analyze their performances. Develop robust portfolio optimization framework including drawdown management for signal weighting and maximizing various utility functions. Research on factor models driving the cash and vol markets; Analyze factors and strategy performance under different market regimes. Develop models for market making taking into consideration fundamental and quantitative features, and historic behavior using statistics, machine learning or heuristics. Work with the business on risk recycle, alpha capture, and devise hedging strategies accordingly. Collaborate broadly with QR teams across regions to build reusable research libraries and tools to back testing and alpha attributions.
Required Qualifications, Capabilities, and Skills:
You have a strong quantitative background, as well as practical problem-solving skills. You have direct working knowledge of signal research with market data and other financial data, alpha capture, risk warehousing, preferrable in equities. You like working closely with trading desks, understanding their business, and have a strong mind-set of ownership to have an impact on the way they operate. You demonstrate proficiency in code design and programming skills, with primary focus on Python, KDB, C++ or Java in a commercial environment. You have practical data analytics skills on real data sets gained through hands-on experience, and can handle and analyze complex, large scale, high-dimensionality data from various sources. You quickly grasp business concepts outside immediate area of expertise and adapt to rapidly changing business needs. You think strategically and creatively when faced with problems and opportunities. You always look for new ways of doing things. Your excellent communication skills, both verbal and written, can engage and influence partners and stakeholders.Preferred Qualifications, Capabilities, and Skills:
Strong graduate degree (MS or PhD) in a quantitative field (Computer Science, Financial Engineering, Mathematics, Physics, Statistics, Economics, …). Strong expertise in statistics and machine learning in financial industry. Robust testing and verification practice. Direct Experience with electronic trading, and knowledge of trading algorithms. 3 to 5 years’ experience in finance: market making, electronic trading, , trading strategies (high to low frequency: market making, statistical arbitrage, option trading…), or derivatives pricing and risk management.Knowledge of equity and volatility products is a plus, but not strict r