{"id":1607194,"url":"https://alion.io/job/evolver-agency-research-scientist-information-theory-and-statistical-inference","title":"Research Scientist - Information Theory and Statistical Inference","company":{"id":155158,"name":"Evolver","domain":"evolver.agency","url":"https://alion.io/company/evolver","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":79,"open_postings":15,"ghost_share":0.267,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":69,"computed_at":"2026-11-03T05:54:40Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Palo Alto, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":153000,"max_usd":340000,"period":"year","method":"role_country_seniority_unknown","sample_n":4425},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"MATLAB","optional":false},{"name":"Python","optional":false},{"name":"AI Agents","optional":true},{"name":"Machine Learning","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"RAG","optional":true}],"status":"live","first_seen_at":"2026-09-10T19:22:30Z","employer_posted_date":"2026-10-06","last_verified_at":"2026-11-03T05:01:21Z","board_verified":true,"closed_at":null,"days_open":53,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":53},"description":"About Evolver\nEvolver 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.\nThe Role \nEvolver is looking for a Research Scientist with a strong background in information theory, statistical inference, uncertainty quantification, or related mathematical methods. \nYou 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. \nWe 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. \nWhat You'll Do \nDevelop methods to quantify information content, information gain, uncertainty, redundancy, and information loss across complex data systems. \nPart of this work is deciding how enterprise data should be represented as graphs, and how well those representations support reasoning.\nDevelop mathematical approaches for determining which observations and data sources are most informative for downstream reasoning and decisions. \nApply information theory, Bayesian inference, statistics, and probabilistic modeling to heterogeneous enterprise data. \nStudy how information is preserved, compressed, combined, or lost as data is transformed into higher-level representations. \nDevelop methods for identifying information gaps and determining the value of acquiring additional evidence. \nBuild algorithms, prototypes, benchmarks, and evaluation methods for information-aware AI systems. \nCollaborate with research, engineering, and product teams to translate new methodologies into real systems. \nMinimum Qualifications \nPh.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). \nStrong foundation in graph theory, such as graph algorithms, combinatorial optimization, graph decomposition, or compact graph representations, demonstrated through a thesis, publications, or research projects.\nStrong mathematical foundation in probability, statistics, information theory, or statistical inference. \nResearch experience in one or more of: \ninformation theory \nentropy and mutual information \nBayesian or statistical inference \nuncertainty quantification \nprobabilistic modeling \nsignal processing \nrepresentation learning \nactive learning or experimental design \nStrong Python, MATLAB, or equivalent scientific computing skills. \nAbility to translate mathematical concepts into computational methods and working prototypes. \nThere 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. \nPreferred Qualifications \nExperience with information bottleneck methods, sufficient statistics, rate-distortion theory, value of information, compressed sensing, Bayesian experimental design, or probabilistic graphical models. \nExperience working with large-scale, noisy, heterogeneous, or partially observed data. \nResearch publications or demonstrated research impact. \nExperience connecting theoretical methods to real-world data or decision-making problems. \nFamiliarity with modern machine learning, representation learning, foundation models, or agentic AI systems. \nDeep prior experience with LLMs, RAG, prompt engineering, or specific agent frameworks is not required. \nApplication \nIn your cover letter, please include the title of your thesis and a brief summary of the problem, methodology, and your specific contribution. \nAbout Evolver \nEvolver is building intelligent enterprise systems that transform large volumes of heterogeneous data into structured information for reasoning and decision-making. \nWe are looking for researchers who want to bring the foundations of information theory, statistical inference, and uncertainty into the next generation of AI.","description_format":"text","description_chars":4544,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Decision Intelligence","Financial AI"],"lifecycle":[{"event":"open","at":"2026-10-01T19:56:20Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 4 · green card filings: 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