| Founded in 1991, Capital Fund Management (CFM) is a successful alternative asset manager and a pioneer in the field of quantitative trading applied to capital markets across the globe. Our methodology relies on statistically robust analysis of terabytes of financial data to inform and enable our asset allocation, trading decisions and automated order execution. CFM offers a challenging and rewarding career for highly-talented and passionate PhDs, IT engineers and financial experts from around the world. Our people can draw on 28 years of research and analysis into financial market data as well as investment in our cutting edge technology platforms. These fundamentals allow us to create and execute state-of-the-art trading strategies for our clients and offer exciting career opportunities for our employees. Our people’s diversity and dedication contribute to CFM’s unique culture of research, innovation and achievement. We are a Great Place to Work company and we offer a collaborative and informal work environment, attractive offices and facilities and highly competitive compensation packages including a profit-based bonus system. The company is regulated by AMF, the SEC and the CFTC, with assets under management of $10 billions. Position: The position involves applied research in financial time series in order to detect and exploit any robust statistical pattern. The aim is to build new strategies, to supplement those already devised and implemented by CFM. You will be working in a team of 55 researchers in close collaboration with software engineers. The work will consist in developing statistical tools, exploiting recent theoretical models, carefully backtesting the robustness through data analysis and implementing them in practice. The candidate should be both creative, in order to imagine new ways of detecting hidden statistical patterns, and rigorous. Although a high interest in finance is crucial, no prior knowledge in the field is needed. The position will be held in Paris or New-York. Ideal Candidate: - PhD in experimental or theoretical science (life science, mathematics, physics, statistics etc.) or in a computational scientific field (big data, computer science, computational biology or chemistry, engineering,
- Post PhD experience (academic or private sector research),
- Taste for analysis of complex data sets, modelling and practical implementations in simulation environments,
- Programming skills in Python, C++,
- Adaptable and rigorous, capable of working in a quickly evolving environment,
- Strong teamwork and communication skills.
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