Evolver
Research Scientist - Information Theory and Statistical Inference
Palo Alto, California
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About the role
- 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.
Responsibilities
- Develop methods to quantify information content, information gain, uncertainty, redundancy, and information loss across complex data systems.
- Develop mathematical approaches for determining which observations and data sources are most informative for downstream reasoning and decisions.
- 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.
- 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.
Requirements
- Research experience in one or more of:
- Ph.D. or thesis-based Master's degree in Electrical Engineering, Applied Mathematics, Statistics, Computer Science, Operations Research,
Benefits
- We combine deep domain expertise, advanced AI, and continuous applied learning to automate complex enterprise workflows while keeping people involved where judgment matters.
Company info
- Part of this work is deciding how enterprise data should be represented as graphs, and how well those representations support reasoning.
This listing is sourced directly from Evolver's careers page and normalized into a canonical job model.