Principal Quantitative Researcher - Finance

The Mitre Corporation•McLean, VA

6d•$173,200 - $259,800•Onsite

About The Position

At MITRE, your passion fuels work that impacts our nation and improves lives. This is where your skills strengthen national security, cybersecurity, health, transportation, and citizen services. We’re a nonprofit engineering, applied research, and advanced technology organization working in the public interest. With objectivity and integrity at our core, we lead federally funded research and development centers for U.S. government agencies – collaborating with industry and academia to deliver integrated, high-impact solutions that advance our nation’s health, security, and prosperity. Our people tackle the toughest technical challenges, supported by competitive benefits, meaningful career development, and a culture of innovation, collaboration, and technical excellence. Choose MITRE for a career with purpose — and help shape the future. Department Summary: MITRE is currently seeking motivated and qualified applicants for a Computational Finance (CompFi) Scientist to join our Financial Innovation Laboratory (FINLab) and Complex Systems Department (L644) in the Modeling and Analysis Technical Division. In FINLab, our CompFi scientists provide expert analysis and consulting to our Federal Government clients in transforming their technologic and regulatory processes to enhance the safety, stability, and security of the US Banking System, Financial Markets, and supporting economic infrastructure. These studies of national significance will be executed in collaboration with industry, academic, and governmental experts and stakeholders. This provides us with unprecedented access to expertise, ideas, and data. You will be a critical Finance subject matter expert (SME) as part of a growing, agile, multidisciplinary team working with academics, the Financial Sector, and US Financial Regulators. Join a highly skilled team executing research, performing analyses, and advising multiple stakeholders, all while maintaining a work-life balance. This is a unique position. Competitive applicants will have demonstrated expertise in at least two of the following areas: Financial Market Microstructure and Regulation, Fintech especially high frequency trading algorithm development and evaluation, Regulatory Tech and Supervisory Tech, Financial Market Analytics, or Market Modeling and Analysis. Ideally, an applicant’s expertise in these areas should be premised upon, or informed by, complexity science to create unique insights into market dynamics as emergent phenomena that are part of a greater complex (in the formal scientific sense) economic system. Furthermore, FINLab leverages a multidisciplinary approach to financial and economic systems analysis. As such, successful candidates will bring that perspective to the position via previous analytic work in related fields, for example a complexity science analysis of socioeconomic or political systems. Furthermore, this position is not specifically forensic in nature. Rather, FINLab and the associated team take the perspective that the economy is a complex adaptive system, and as such is nonstationary. Historical analysis will only get us so far, we also need to employ tools and techniques, e.g., agent-based computational economics, that allow us to explore the generative mechanisms that underlie markets and market dynamics. MITRE created the FINLab to enable bold thinking, new ideas, and risk taking in order to bring new insights and new possibilities to our Federal government partners.

Requirements

Typically requires a minimum of 10 years of related experience with a Bachelor’s degree; or 8 years and a Master’s degree; or a PhD with 5 years’ experience; or equivalent combination of related education and work experience.
Published research in computational finance, complexity economics or quantitative finance, broadly defined.
Very strong academic credentials in quantitative or computational finance, or related field with substantial academic work in the simulation and analysis of equity market dynamics.
Experienced researcher as evidenced by a peer reviewed publication record with associated H-index of 10 or more. While a strong publication record is ideal, MITRE realizes that not all well suited applicants have been in positions affording them the opportunity to publish. If this describes your situation, we still welcome your application.
Demonstrated experience working on or leading customer facing engagements emphasizing quantitative or computational finance.
Demonstrated ability to manipulate large financial datasets and time series data and perform calculations and create visualizations with at least one modern programming language like Python (utilizing packages like scikit-learn, pandas, or dask), R (utilizing packages like caret, dplyr, or data.table), or other modern programming language.
Ability to bridge finance, computational, and data analytic domains.
Thrives in working in a technical environment with multidisciplinary teams on critical national security challenges.
Passion for developing new technology and analytics for solving national challenges.
Excellent analytic writing and verbal/presentation skills to senior leaders.
Excellent interpersonal skills, and effective working relationships with internal and external customers.
Ability to apply, modify and formulate algorithms, to include high frequency trading algorithms, and processes to solve computational financial problems.
Must be eligible to obtain and maintain a Secret U.S Government issued Security Clearance. Per the U.S. Government’s eligibility requirements, you must be a U.S Citizen to be considered for a security clearance.
This position requires a minimum of 3 days on-site.

Nice To Haves

PhD in a quantitative discipline, with deep knowledge of financial markets and market dynamics.
Experience with U.S. Treasury, Financial Regulators, or with the commercial side such as the Banking or Finance Industry.
Experience performing novel market research and analyses as evidenced by scientific publication.
Research experience with the global financial system.
Experience leading teams in a technical environment and delivering meaningful impacts in computational finance, as measured by securing and managing extramural funding in excess of one million dollars.
Experience with building and shaping research programs via successful competition for extramural funding in excess of one million dollars.
Ability to bridge finance, computational, economics, and data analytic domains. While this position is centered upon financial systems and their dynamics, the ideal candidate will bring a multidisciplinary perspective, in terms of tools and techniques, to this position to include topics such as complex systems, complexity economics, or ergodic economics.
Familiarity working in cloud-based computing environments like Amazon Web Services, Google Cloud, or Microsoft Azure.
Experience applying various machine learning approaches (e.g., random forest, neural networks, support vector machines).
Experience working with databases (e.g., PostgreSQL, Oracle, MySQL, MongoDB, Neo4J).
Experience using version control (e.g., Git, Mercurial, SVN) to support collaborative development.
Experience utilizing notebooks (e.g., Jupyter, R Markdown, Zeppelin).

Responsibilities

Develop innovative, multidisciplinary approaches for analyzing financial data and trends that may impact the US financial system and markets.
Provide expert analysis and/or develop research proposals on issues related to improving financial regulation and banking supervision, adopting new technologies to support distributed ledger or faster payments, or implementing advanced financial analytics.
Understand US, mission partner, and adversary capabilities and assess potential threats to U.S. financial stability and security.
Conduct analytic and simulation-based analyses using financial data to provide new insights which support policy-level decision making.
Provide US Treasury and related sponsors with commercial and market analysis of national interest areas, and methodologies to identify mitigation alternatives.