Digital Asset Market Risk M&S Analyst

The MITRE Corporation•New Bedford, MA

25d•$124,400 - $186,600•Hybrid

About The Position

MITRE’s Model Based Analytics Department is an interdisciplinary department that thrives on having a large toolbox to support data- and model-driven decision making and we value people who can bring diverse perspectives. Our employees are expected to work on multiple projects, cross-pollinate good ideas across the government and continuously learn. Our department works to solve modeling and analytic problems in the public interest, in partnership with the government. We also actively conduct independent research in areas of interest to our government partners. We are seeking a motivated analyst to support the development of advanced analytical, computational, and simulation capabilities focused on digital asset markets. This role is centered on better characterizing, assessing, and predicting digital asset market dynamics from regulatory, supervisory, and risk management perspectives. The ideal candidate will bring strong quantitative and analytical skills, intellectual curiosity, ability to work in a fast-paced and dynamic environment, and an interest in applying modeling and simulation methods to complex, emerging financial systems. This work will support government sponsors in understanding risks, evaluating policy and oversight challenges, and developing actionable insights related to digital assets and market structure. You will work on mission-driven problems at the intersection of digital assets, financial innovation, and public-interest technology. You will collaborate with government sponsors and interdisciplinary teams to develop rigorous, actionable analysis that informs regulatory and risk management decisions in a rapidly evolving domain.

Requirements

Bachelor Degree in quantitative discipline such as Data Science, Operations Research, Mathematics, Statistics, Complex Systems Modeling, Computational Social Science; with significant experience in one or more of the following disciplines: game theory, experimental economics, behavioral economics, micro/macroeconomics, finance, financial engineering, web networking, software engineering, distributed computing.
Minimum 5 years of experience with a bachelor’s degree, or 3 years with a Master’s degree, or a PhD with relevant hands-on experience with decentralized banking ecosystem.
Willingness to adapt, learn new methods, and contribute across a range of sponsor-driven problem sets in digital asset markets, financial systems, and emerging risk analysis.
Experience developing research questions, problem statements, testing hypotheses, evaluating outcomes/impacts and/or working in open-ended analytical environments.
Familiarity with computational modeling, simulation, statistical analysis, or applied quantitative research.
Ability to work collaboratively with government sponsors and multidisciplinary teams to understand complex challenges and evaluate solution options.
Strong written and verbal communication skills, including the ability to convey technical findings in an intuitive, actionable manner that can be understood by all audiences, regardless of technical expertise.
Ability to take initiative, work independently, and be a collaborative teammate.
This position requires a minimum of 50% hybrid on-site
Top Secret clearance required upon hire.
Top Secret clearance required within one year of hire.

Nice To Haves

PhD in quantitative discipline such as Data Science, Operations Research, Mathematics, Statistics, Complex Systems Modeling, Computational Social Science; with significant experience in one or more of the following disciplines: game theory, experimental economics, behavioral economics, micro/macroeconomics, finance, financial engineering, web networking, software engineering, distributed computing.
Prior experience in conducting agent-based modeling, micro-simulation, or market simulation.
Expertise in financial contagion modeling, stress testing, or systemic risk analysis.
Some familiarity with market microstructure and its function.
Prior experience analyzing and interacting with blockchain ecosystems, DeFi protocols, and stablecoins.
Prior experience with market surveillance, anomaly detection, or empirical market microstructure research.
Expertise in translating policy or regulatory concepts into analytical or computational frameworks.
Technical publication in scientific, financial, or digital asset-related venues.

Responsibilities

Characterizing risks associated with digital asset, token, and stablecoin design.
Assessing market structure, participant behavior, liquidity conditions, and sources of fragility across digital asset ecosystems.
Analyzing blockchain and market data to identify anomalous behavior, market manipulation, concentration, and emerging vulnerabilities.
Designing and building agent-based models, scenario-based simulations, and stress-testing frameworks for digital asset markets and protocols.
Developing novel empirical measures to evaluate market quality, systemic risk, contagion, resilience, and consumer protection concerns.
Applying advanced analytics and computational models to inform regulatory, supervisory, and risk management challenges facing MITRE sponsors.
Developing analytical frameworks to identify, measure, and monitor risks in digital asset markets, including market, liquidity, counterparty, operational, conduct, and systemic risks
Producing evidence and analysis to support real-time market monitoring, surveillance, supervisory instrumentation, and consumer protection strategies
Supporting the development, evaluation, and testing of digital asset risk assessment frameworks, controls, and standards
Formulating agent-based models, micro-simulations, scenario analyses, and stress tests to explore market outcomes driven by participant behavior, incentive structures, and protocol design
Translating policy, regulatory, and supervisory directives into computational rules, model constraints, and measurable indicators
Modeling and simulating contagion dynamics in digital asset markets, including stablecoin stress events, liquidity shocks, runs, deleveraging, interconnections across platforms and protocols, and feedback effects between on-chain and off-chain markets
Analyzing drivers of instability such as concentration, manipulation, reflexive trading behavior, and liquidity pool dynamics
Integrating on-chain, off-chain, transactional, and market-structure data to support empirical analysis and model development
Validating model outputs using backtesting, calibration, sensitivity analysis, benchmark comparison, and expert review
Presenting findings in clear, intuitive, and actionable ways for technical and non-technical audiences
Supporting projects across the full lifecycle, including concept development, requirements definition, data acquisition, model development, integration, validation, and stakeholder engagement