Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped our collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

Your future role within QRT

• Your core objective is to create high quality predictive signals.
• By leveraging access to large and diversified datasets you will identify statistical patterns and opportunities.
• Share and discuss research results, methodology, data sets and processes with other researchers.
• Implement the signals and the relevant datasets within the global execution platform.
• Monitor signal behaviour and model performance over time.
• You would lead the full strategy research cycle from signal generation to implementation.

Your present skillset

• Advanced degree in a quantitative field such as data science, statistics, mathematics, physics or engineering.
• Strong knowledge in statistics, machine learning, NLP or AI techniques is a plus.
• Capacity to multi-task in a fast paced environment while keeping strong attention to detail.
• Coding skills required in at least one leading programing language (Python, R, Matlab and /or C++, C#).
• Experience in exploring large datasets across multiple time frames is a plus.
• Intellectual curiosity to explore new data sets, solve complex problems, drive innovative processes and connect the dots between multiple fields.
• Capacity to work with autonomy within a collegial and collaborative environment.
• Strong capacity to communicate with technologists, data scientists and traders across the globe.
• Proven track record in delivering successful systematic strategies.

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.

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.

JOB DESCRIPTION

Researchers are responsible for independently conducting quantitative finance research with a focus on statistical and predictive models. Successful researchers manage all aspects of the research process including methodology selection, data collection and analysis, testing, prototyping, backtesting, and performance monitoring.

Some successful researchers have joined us from similar backgrounds at other firms. Others have joined from related fields or directly from academia and have thrived with hands on guidance from our large team of experienced portfolio managers and researchers. Our most exceptional team members combine strong technical skills and a passion for problem solving with an intense curiosity about financial markets and human behavior.

DESIRABLE CANDIDATES

• MS or PhD candidates in finance, computer science, mathematics, physics, or other quantitative discipline
• 3-7 years of experience in alpha driven quantitative research for equities, futures, fixed income, credit, and/or FX
• Strong analytical and quantitative skills
• Demonstrated ability to conduct independent research utilizing large data sets
• Programming in any of the following: C++, Java, C#, MATLAB, R, Python, or Perl
• Detail-oriented
• Willing to take ownership of his/her work, working both independently and within a small team

We’re looking for exceptional colleagues with unparalleled passion. If you’d like your resume to stand out, tell us about your exceptional personal achievements, even if they have nothing to do with finance. Of course we love to hear more about specific engineering or data projects that you’ve worked outside of school, or as part of your curriculum. If you’re proud of the work you did we want to hear about it. In addition to exceptional statisticians and engineers, we work with talented musicians, writers, mathematicians, and founders of non-profits; we’d love to learn more about what excites you.