Research Scientist - Information Theory and Statistical Inference

Evolver • Palo Alto, CA

6d

About The Position

Evolver is seeking a Research Scientist with a strong background in information theory, statistical inference, uncertainty quantification, or related mathematical methods. The role involves developing methodologies for understanding how large-scale, heterogeneous enterprise data can be transformed into decision-relevant information, how uncertainty changes with new evidence, and how the value of information can be quantified. Applications are welcome from exceptional recent graduates and experienced researchers, with a focus on mathematical depth, research ability, and original thinking over years of industry experience.

Requirements

• 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 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.

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.

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.
• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level