Research Scientist: Information Theory & Uncertainty

Evolver; Palo Alto (CA); On-site; USD 170,000 - 250,000; Full time

Job summary

Evolver is seeking a Research Scientist with a deep background in information theory, probabilistic modeling, and statistical inference to advance methods for transforming large-scale enterprise data into actionable information.

You will develop information-content metrics, quantify uncertainty as new evidence arrives, and design algorithms that guide data representation, graph structures, and decision-making in complex systems.

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.
• Strong foundation in graph theory demonstrated through a thesis, publications, or research projects.
• Strong mathematical foundation in probability, statistics, information theory, or statistical inference.
• Research experience in information theory, entropy and mutual information, Bayesian or statistical inference, uncertainty quantification, probabilistic modeling, or related areas.
• Experience with information bottleneck methods, rate-distortion theory, probabilistic graphical models, or Bayesian experimental design is a plus.

Responsibilities

• Develop methods to quantify information content, information gain, uncertainty, redundancy, and information loss across complex data systems.
• Decide how enterprise data should be represented as graphs and how those representations support reasoning.
• Develop approaches to determine 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.
• 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.

Skills

Graph theory

Probability & statistics

Information theory

Bayesian inference

Statistical inference

Python / MATLAB

Education

PhD or Master’s in a quantitative field

Tools

Python

MATLAB

Job description

Evolver is seeking a Research Scientist with a deep background in information theory, probabilistic modeling, and statistical inference to advance methods for transforming large-scale enterprise data into actionable information.

You will develop information-content metrics, quantify uncertainty as new evidence arrives, and design algorithms that guide data representation, graph structures, and decision-making in complex systems.