ARPM – Advanced Risk and Portfolio Management is a privately held research and education company, directed by Attilio Meucci, based in New York City with virtual offices world-wide. ARPM’s mission is to set and disseminate the standards for advanced quantitative risk management and quantitative  portfolio management across the financial industry: asset management, banking, and insurance.

ARPM is looking for a new:

Researcher-in-training 

or a minimum period of 6 months, indefinitely extensible.  

The successful candidate will review and code practical case studies and theoretical examples in quantitative finance, contributing to the ARPM Lab. The successful candidate will work full-time, remotely (from home or any other location), constantly communicating via multi-media with the other members of ARPM. 

The ARPM researcher-in-training position represents an opportunity for candidates with strong academic background, who wish to apply to real problems in finance the rigorous, research-oriented approach acquired in their schooling. 

The progression:

ARPM emphasizes the constant intellectual growth of its resources. For the first 6 months the researcher-in-training will be focused on specific projects. At the end of this period (s)he will conduct a presentation on the topics covered. 

Then, (s)he will start broadening his/her scope, attending the presentations of their peers and seniors, working on broader projects, and acquiring hands-on-knowledge of all the topics of the ARPM Lab. The approximate time required to attain the required level of familiarity with the ARPM Lab is: two years for a recent master’s graduate; one year for a recent PhD graduate. 

When ready, the researcher-in-training will be tested on all such topics with an exam. If successful, (s)he will conclude his/her training period, attaining the title of ARPM researcher. The ARPM researcher will then engage in highly quantitative projects with ARPM clients, becoming a profit center. 

The candidate:

• Master’s or PhD degree in mathematics, theoretical physics, electrical engineering, or related disciplines
• Good knowledge of statistics and probability
• Proficiency in Python or similar programming languages
• Good command of English
• No knowledge of financial markets is necessary
• Reference: N8ojUjG_pUlT2aSj9ZKNEzaNLHZnFQ6G1l1MiBMgP2Nd2kkYAFV7r5rcPp__5oED

Compensation:

• Competitive

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