Job description

About Evolver Evolver is an AI technology company transforming professional services. We combine deep domain expertise, advanced AI, and continuous applied learning to automate complex enterprise workflows while keeping people involved where judgment matters. Our goal is to help organizations operate more efficiently, accurately, and intelligently. The Role Evolver is looking for a Research Scientist with a strong background in information theory, statistical inference, uncertainty quantification, or related mathematical methods. You will help develop methodologies for understanding how large-scale, heterogeneous enterprise data can be transformed into decision-relevant information, how uncertainty changes as new evidence is acquired, and how the value of information can be quantified. We welcome applications from exceptional recent graduates as well as experienced researchers. We care more about mathematical depth, research ability, and original thinking than years of industry experience. What You'll Do Develop methods to quantify information content, information gain, uncertainty, redundancy, and information loss across complex data systems. Part of this work is deciding how enterprise data should be represented as graphs, and how well those representations support reasoning. Develop mathematical approaches for determining which observations and data sources are most informative for downstream reasoning and decisions. Apply information theory, Bayesian inference, statistics, and probabilistic modeling to heterogeneous enterprise data. Study how information is preserved, compressed, combined, or lost as data is transformed into higher-level representations. Develop methods for identifying information gaps and determining the value of acquiring additional evidence. Build algorithms, prototypes, benchmarks, and evaluation methods for information-aware AI systems. Collaborate with research, engineering, and product teams to translate new methodologies into real systems. Minimum Qualifications Ph.D. or thesis-based Master's degree in Electrical Engineering, Applied Mathematics, Statistics, Computer Science, Operations Research, Information Science, Signal Processing, or a related quantitative field (Note: Please include the title of your thesis in your application and a brief summary of the problem, methodology, and your specific contribution). Strong foundation in graph theory, such as graph algorithms, combinatorial optimization, graph decomposition, or compact graph representations, demonstrated through a thesis, publications, or research projects. Strong mathematical foundation in probability, statistics, information theory, or statistical inference. Research experience in one or more of: information theory entropy and mutual information Bayesian or statistical inference uncertainty quantification probabilistic modeling signal processing representation learning active learning or experimental design Strong Python, MATLAB, or equivalent scientific computing skills. Ability to translate mathematical concepts into computational methods and working prototypes. There is no minimum number of years of industry experience. Strong candidates may demonstrate their capabilities through a thesis, publications, research projects, internships, open-source work, or relevant industry experience. Preferred Qualifications Experience with information bottleneck methods, sufficient statistics, rate-distortion theory, value of information, compressed sensing, Bayesian experimental design, or probabilistic graphical models. Experience working with large-scale, noisy, heterogeneous, or partially observed data. Research publications or demonstrated research impact. Experience connecting theoretical methods to real-world data or decision-making problems. Familiarity with modern machine learning, representation learning, foundation models, or agentic AI systems. Deep prior experience with LLMs, RAG, prompt engineering, or specific agent frameworks is not required. Application In your cover letter, please include the title of your thesis and a brief summary of the problem, methodology, and your specific contribution. About Evolver Evolver is building intelligent enterprise systems that transform large volumes of heterogeneous data into structured information for reasoning and decision-making. We are looking for researchers who want to bring the foundations of information theory, statistical inference, and uncertainty into the next generation of AI.

Requirements

Must-haves

Evolver is looking for a Research Scientist with a strong background in information theory, statistical inference, uncertainty quantification, or related mathematical methods.
We welcome applications from exceptional recent graduates as well as experienced researchers. We care more about mathematical depth, research ability, and original thinking than years of industry experience.
Develop methods to quantify information content, information gain, uncertainty, redundancy, and information loss across complex data systems.
Part of this work is deciding how enterprise data should be represented as graphs, and how well those representations support reasoning.
Develop mathematical approaches for determining which observations and data sources are most informative for downstream reasoning and decisions.
Apply information theory, Bayesian inference, statistics, and probabilistic modeling to heterogeneous enterprise data.
Study how information is preserved, compressed, combined, or lost as data is transformed into higher-level representations.
Develop methods for identifying information gaps and determining the value of acquiring additional evidence.
Build algorithms, prototypes, benchmarks, and evaluation methods for information-aware AI systems.
Collaborate with research, engineering, and product teams to translate new methodologies into real systems.
Strong foundation in graph theory, such as graph algorithms, combinatorial optimization, graph decomposition, or compact graph representations, demonstrated through a thesis, publications, or research projects.
Strong mathematical foundation in probability, statistics, information theory, or statistical inference.
information theory
entropy and mutual information
Bayesian or statistical inference
uncertainty quantification
probabilistic modeling
signal processing

Nice-to-haves

Experience with information bottleneck methods, sufficient statistics, rate-distortion theory, value of information, compressed sensing, Bayesian experimental design, or probabilistic graphical models.
Experience working with large-scale, noisy, heterogeneous, or partially observed data.
Research publications or demonstrated research impact.
Experience connecting theoretical methods to real-world data or decision-making problems.
Familiarity with modern machine learning, representation learning, foundation models, or agentic AI systems.
In your cover letter, please include the title of your thesis and a brief summary of the problem, methodology, and your specific contribution.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipythonmachine learning

Platform: greenhouse
Effort: quick