Modeling Scientist

Anthos is building causal decision infrastructure for capital allocators, investors, philanthropies and policymakers. By mapping cause and effect within complex systems, it supports strategic planning and forecasting for higher-stakes decisions.

The platform ingests the relevant literature, reports, and data, then applies causal modeling and neuro-symbolic AI to build math-based simulation models. These are auditable, and grounded in explicit, rigorous equations rather than AI-generated text. Agentic assistants sit on top, so non-technical users can run simulations and assess the efficacy of a planned allocation or policy proposal.

Organizations like the World Bank use Anthos to make the case for investing in more resilient education infrastructure, and major utilities have been using it for strategic grid planning and VPP deployment strategy.

Anthos Systems is looking for a Modeling Scientist to join our team. In this individual contributor role, you'll work on building and advancing causal models for clients ranging from finance and energy to climate and policy. This is an opportunity to work at the cutting edge of AI and modeling.

• Design, build, and evaluate statistical models to solve applied business and research problems
• Conduct original research in AI, staying current with relevant literature and techniques
• Analyze datasets to extract actionable insights and support data-driven decision-making
• Collaborate with engineering and product to translate research findings into deployable solutions
• Develop data pipelines, experiments, and model evaluation frameworks
• Communicate findings clearly to clients, both technical and non-technical stakeholders
• Maintain strict confidentiality of Company data, trade secrets, and proprietary research in accordance with the Company's Proprietary Information, Inventions, Non-Competition and Non-Solicitation Agreement

• Graduate degree in Computer Science, Data Science, Statistics, Mathematics, or a related field (or equivalent professional experience)
• Expertise in several of the following: structural causal models, mixed effect regression models, dynamic bayesian networks, discrete time equations, economic modeling, control theory, system dynamics.
• Grasp of Bayesian statstics
• Proficiency in Python, R or Julia (or equivalent) and standard data science tooling
• Strong analytical, communication, and collaboration skills
• Expertise in energy, finance, and policy a plus.
  

• Salary: Market Rates
• Position is freelance with potential to become a full-time opportunity.