Complex Systems Modeler
Role details
Job location
Tech stack
Requirements
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Be passionate about formulating and developing models and simulations of real world problems involving complex dynamic systems.
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Have an innate curiosity and interest in developing research questions and testing hypotheses with open ended tasking.
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Work with a spectrum of government sponsors to gain understanding of their challenges, evaluate possible solutions, and conduct insightful, actionable analyses.
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Support development and application of a variety of analytic models to sponsor challenges, with a willingness to adapt and learn in a fast-paced environment.
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Present results in an intuitive, actionable manner that can be understood by all audiences, regardless of technical expertise.
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Remain current on open-source, industry, academia, and US Government techniques and tools for modeling, analysis, data science, visualization, and engineering.
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Take initiative in owning aspects of your work and be a collaborative teammate.
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Bachelor degree in quantitative field such as Data Science, Computational Social Science, Mathematics, Statistics, Operations Research, Geospatial Science, Computer Science, Software Engineering, Economics.
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Or Bachelor degree in Social/Behavioral Sciences (sociology, psychology, political science, public health) paired with significant computational experience.
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Minimum of 2 years of experience with Bachelor degree, or Master's Degree with relevant hands-on experience in computational modeling and analysis.
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Excellent written and verbal communication skills.
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Demonstrated ability to formulate rigorous models based on loosely defined or underspecified research questions and requirements.
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Demonstrated expertise in using Agentic-AI and at one or more of the following paradigms: agent-based modeling, discrete event modeling, system dynamics, or applied statistical modeling/machine learning for complex systems.
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Applied experience in modeling and analysis of spatiotemporal datasets.
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Experience manipulating large datasets with at least one modern programming language or business intelligence platform (e.g., Python, R , SAS, MATLAB, Java, C++).
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Experience leveraging COTS tools or writing programs to visualize multi-dimensional data using tools like Tableau, ggplot2, Plotly, matplotlib, seaborn, or D3.js.
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Ability to apply, modify, and formulate algorithms and processes to solve challenging problems.
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Possesses an active U.S. government clearance of a Secret of above with the ability to obtain and maintain Top Secret/SCI clearance.
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Per the U.S. Government's eligibility requirements for a clearance, U.S Citizenship is required.
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This position requires a minimum of 50% hybrid on-site.
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Master's degree in quantitative field such as Data Science, Computational Social Science, Mathematics, Statistics, Operations Research, Geospatial Science, Computer Science, Software Engineering, Economics.
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Or Master's degree in Social/Behavioral Sciences (sociology, psychology, political science, public health) paired with significant hands-on computational experience.
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Experience leading or conducting research in computational social/behavioral sciences or natural sciences.
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Experience developing models using multiple methodologies.
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Demonstrated experience leading stakeholder/funder facing engagements and providing relevant day-to-day tasking for one or more junior staff.
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Applied experience developing interactive visualizations or configuring dashboard applications using open source web technologies (e.g., Angular, Vue, react, D3.js) or other frameworks (e.g., Shiny, Plotly Dash).
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Prior experience working with databases (e.g., PostgreSQL, Oracle, MySQL, MongoDB, Neo4j).
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Prior experience developing programmatic solutions in a collaborative environment (e.g., Git, Mercurial, SVN).
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Familiarity with ArcGIS or other GIS software or analytic tools.
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Experience using notebooks (e.g., Jupyter, R Markdown, Zeppelin).
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Experience developing data-intensive full stack containerized web applications using Node.js, Flask, Django or other technologies or demonstrated ability manipulating large datasets and time series data.
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Comfort with modern AI tools (e.g., large language models) and their application, evaluation, and validation.
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Active TS, TS/SCI or TS/SCI with polygraph.