{"version":"0.1","company":{"name":"YubHub","url":"https://yubhub.co","jobsUrl":"https://yubhub.co/jobs/skill/bayesian-inference"},"x-facet":{"type":"skill","slug":"bayesian-inference","display":"Bayesian Inference","count":3},"x-feed-size-limit":100,"x-feed-sort":"enriched_at desc","x-feed-notice":"This feed contains at most 100 jobs (the most recently enriched). For the full corpus, use the paginated /stats/by-facet endpoint or /search.","x-generator":"yubhub-xml-generator","x-rights":"Free to redistribute with attribution: \"Data by YubHub (https://yubhub.co)\"","x-schema":"Each entry in `jobs` follows https://schema.org/JobPosting. YubHub-native raw fields carry `x-` prefix.","jobs":[{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_3e0a4df0-973"},"title":"Principal Applied Scientist","description":"<p>This role is part of the Microsoft Search &amp; Ads Network (MSAN) modeling team, focused on building large-scale machine learning systems for ads retrieval, ranking, user understanding and marketplace optimization across different surfaces. The team develops end-to-end models that predict user engagement and advertiser value – powering candidate generation, relevance scoring, and serving stack ranking that directly impact ad quality, delivery efficiency, and revenue. Responsibilities span the full modeling lifecycle, including training data and labeling strategy, feature and signal design, model development, and rigorous offline and online evaluation. Engineers and applied scientists work closely at the intersection of machine learning, economics, and large-scale systems to deliver high-performance real-time inference and robust experimentation in production.</p>\n<p>As a Principal Applied Scientist, you will have a solid background in Machine Learning, Reinforcement Learning, Causal Inference, Data Science, Data Mining, or related field. You will play a key role in driving algorithmic improvements to online and offline systems, develop and deliver robust and scalable solutions, make direct impact to both user and advertisers experience, and continually increase the revenue for Bing ads.</p>\n<p>Qualifications:</p>\n<ul>\n<li>Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)</li>\n<li>Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research)</li>\n<li>Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)</li>\n<li>Equivalent experience</li>\n</ul>\n<p>Preferred Qualifications:</p>\n<ul>\n<li>Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research)</li>\n<li>Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)</li>\n<li>Equivalent experience</li>\n</ul>\n<p>Research experience (publications) in the following areas: statistical machine learning, deep learning, data mining, causal inference, information retrieval, and Bayesian inference. 2+ years of experience in any of the following areas: ads retrieval and ranking system, statistical machine learning, deep learning, data mining, causal inference, information retrieval, game theory, mechanism design, optimization and Bayesian inference. Proficient problem solving and data analysis skills. Proficient software design and development skills/experience.</p>\n<p>#MicrosoftAI Applied Sciences IC5 – The typical base pay range for this role across Canada is CAD $142,400 – CAD $257,500 per year. Find additional pay information here: https://careers.microsoft.com/v2/global/en/canada-pay-information.html</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_3e0a4df0-973","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Microsoft","sameAs":"https://microsoft.ai","logo":"https://logos.yubhub.co/microsoft.ai.png"},"x-apply-url":"https://microsoft.ai/job/principal-applied-scientist-30/","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"CAD $142,400 – CAD $257,500 per year","x-skills-required":["Machine Learning","Reinforcement Learning","Causal Inference","Data Science","Data Mining","Statistics","Econometrics","Computer Science","Electrical or Computer Engineering"],"x-skills-preferred":["statistical machine learning","deep learning","information retrieval","Bayesian inference","ads retrieval and ranking system","game theory","mechanism design","optimization"],"datePosted":"2026-04-24T12:11:59.042Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Vancouver"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Machine Learning, Reinforcement Learning, Causal Inference, Data Science, Data Mining, Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, statistical machine learning, deep learning, information retrieval, Bayesian inference, ads retrieval and ranking system, game theory, mechanism design, optimization","baseSalary":{"@type":"MonetaryAmount","currency":"CAD","value":{"@type":"QuantitativeValue","minValue":142400,"maxValue":257500,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_c00d615d-431"},"title":"Principal Applied Scientist","description":"<p>This role is part of the Microsoft Search &amp; Ads Network (MSAN) modeling team, focused on building large-scale machine learning systems for ads retrieval, ranking, user understanding and marketplace optimization across different surfaces. The team develops end-to-end models that predict user engagement and advertiser value – powering candidate generation, relevance scoring, and serving stack ranking that directly impact ad quality, delivery efficiency, and revenue. Responsibilities span the full modeling lifecycle, including training data and labeling strategy, feature and signal design, model development, and rigorous offline and online evaluation. Engineers and applied scientists work closely at the intersection of machine learning, economics, and large-scale systems to deliver high-performance real-time inference and robust experimentation in production.</p>\n<p>As a Principal Applied Scientist, you will have a solid background in Machine Learning, Reinforcement Learning, Causal Inference, Data Science, Data Mining, or related field. You will play a key role in driving algorithmic improvements to online and offline systems, develop and deliver robust and scalable solutions, make direct impact to both user and advertisers experience, and continually increase the revenue for Bing ads.</p>\n<p>Qualifications:</p>\n<ul>\n<li>Bachelor’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)</li>\n<li>Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research)</li>\n<li>Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)</li>\n<li>Equivalent experience</li>\n</ul>\n<p>Preferred Qualifications:</p>\n<ul>\n<li>Master’s Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research)</li>\n<li>Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)</li>\n<li>Equivalent experience</li>\n</ul>\n<p>Research experience (publications) in the following areas: statistical machine learning, deep learning, data mining, causal inference, information retrieval, and Bayesian inference. 2+ years of experience in any of the following areas: ads retrieval and ranking system, statistical machine learning, deep learning, data mining, causal inference, information retrieval, game theory, mechanism design, optimization and Bayesian inference. Proficient problem solving and data analysis skills. Proficient software design and development skills/experience.</p>\n<p>#MicrosoftAI Applied Sciences IC5 – The typical base pay range for this role across Canada is CAD $142,400 – CAD $257,500 per year.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_c00d615d-431","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Microsoft","sameAs":"https://microsoft.ai","logo":"https://logos.yubhub.co/microsoft.ai.png"},"x-apply-url":"https://microsoft.ai/job/principal-applied-scientist-31/","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Machine Learning","Reinforcement Learning","Causal Inference","Data Science","Data Mining","Statistics","Econometrics","Computer Science","Electrical or Computer Engineering"],"x-skills-preferred":["statistical machine learning","deep learning","information retrieval","Bayesian inference","ads retrieval and ranking system","game theory","mechanism design","optimization"],"datePosted":"2026-04-24T12:11:19.587Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Vancouver"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Machine Learning, Reinforcement Learning, Causal Inference, Data Science, Data Mining, Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, statistical machine learning, deep learning, information retrieval, Bayesian inference, ads retrieval and ranking system, game theory, mechanism design, optimization"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_c01f9dc6-b17"},"title":"Data Scientist - Staff or Senior (United Kingdom)","description":"<p>In this role, you will build predictive models and apply scientific computing, statistical, and physics-based methods to find places with evidence of ore-forming processes and predict locations of ore-grade mineralization in 2D and 3D.</p>\n<p>You will help build a worldwide dataset for our exploration program, with careful attention to identifying and quantifying uncertainty in the data and predictions.</p>\n<p>You will create models and develop software to accelerate discovery of critical battery metals.</p>\n<p>You will join an outstanding team of data scientists and engineers and work closely with (*applicant&#39;s) world-renowned geoscientists to incorporate our best understanding of the chemical and physical processes that create ore deposits.</p>\n<p>Working with your geoscience colleagues, you will create 2D and 3D geologic predictions, identify exploration targets, design field programs to collect data, and use that data to reduce uncertainty in our predictions and guide the next phase of field work.</p>\n<p>Ultimately, your role is to help KoBold make valuable discoveries by building data tools to solve scientific problems.</p>\n<p>As one of the early members of this team, you will help build these tools from the ground up.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Help develop KoBold&#39;s proprietary software exploration tools.</li>\n<li>Find and curate geophysical, geochemical, geologic, and geographic data and integrate it into KoBold&#39;s proprietary data system.</li>\n<li>Build models to make statistically valid predictions about the locations of compositional anomalies within the Earth&#39;s crust.</li>\n<li>Create effective visualizations for evaluating model performance and enabling rapid interaction with the underlying data and key features.</li>\n<li>Develop and apply data processing, statistical, and physics-based techniques to geoscientific data , from computer vision to geophysical inversions , and use the results to guide our targeting efforts and inform our acquisition and exploration decisions.</li>\n<li>Present to and collaborate with our external partners and stakeholders.</li>\n</ul>\n<p>Qualifications:</p>\n<ul>\n<li>Technical skills, including extensive experience with Python&#39;s data science packages and general software engineering practices.</li>\n<li>Collaborative software development (git), and familiarity with software engineering best practices like unit test / integration test suites, and CICD pipelines.</li>\n<li>Cloud computing resources.</li>\n<li>Building predictive models, applying them to different problems, and evaluating and interpreting the results.</li>\n<li>Data from a variety of physical systems.</li>\n<li>Geospatial analyses and visualizations.</li>\n</ul>\n<p>Technical knowledge:</p>\n<ul>\n<li>Broad skills in and knowledge of applied statistics and Bayesian inference.</li>\n<li>Substantial understanding of machine learning algorithms.</li>\n</ul>\n<p>Training and work experience:</p>\n<ul>\n<li>An advanced degree in the physical sciences, engineering, computer science, or mathematics.</li>\n<li>A minimum work experience of 4 years post PhD or 8 years post MS, ideally as a data scientist or data engineer.</li>\n<li>Experience leading technical teams to apply novel scientific approaches to core business problems.</li>\n</ul>\n<p>Work practices and motivation:</p>\n<ul>\n<li>Ability to take ownership and responsibility of large projects.</li>\n<li>Ability to explain technical problems to and collaborate on solutions with domain experts.</li>\n<li>Communicates well on a collaborative, cross-functional team.</li>\n<li>Excitement about joining a fast-growing early-stage company, comfort with a dynamic work environment, and eagerness to take on a range of responsibilities.</li>\n<li>Ability to independently prioritize multiple tasks effectively.</li>\n<li>Intellectual curiosity and eagerness to learn about all aspects of mineral exploration, particularly in the geology domain.</li>\n<li>Enjoys constantly learning such that you are driving insights through using our tools in exploration and willing to work directly with geologists in the field.</li>\n<li>Keen not just to build cool technology, but to figure out what technical product to build to best achieve the business objectives of the company.</li>\n<li>A valid passport and willingness to travel to observe our work at Mingomba or at an exploration site around the world.</li>\n</ul>\n<p>Preferred skills include creating machine learning models on geospatial data, geostatistics, image processing or computer vision, and distributed computing applications for machine learning and other computations.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_c01f9dc6-b17","directApply":true,"hiringOrganization":{"@type":"Organization","name":"KoBold Metals","sameAs":"https://www.koboldmetals.com/","logo":"https://logos.yubhub.co/koboldmetals.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/koboldmetals/jobs/4677631005","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$140,00 - $240,000 (USD) plus equity and benefits","x-skills-required":["Python's data science packages","General software engineering practices","Collaborative software development (git)","Software engineering best practices","Cloud computing resources","Building predictive models","Applying models to different problems","Evaluating and interpreting results","Data from a variety of physical systems","Geospatial analyses and visualizations","Applied statistics and Bayesian inference","Machine learning algorithms"],"x-skills-preferred":["Creating machine learning models on geospatial data","Geostatistics","Image processing or computer vision","Distributed computing applications for machine learning and other computations"],"datePosted":"2026-04-17T12:40:11.334Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python's data science packages, General software engineering practices, Collaborative software development (git), Software engineering best practices, Cloud computing resources, Building predictive models, Applying models to different problems, Evaluating and interpreting results, Data from a variety of physical systems, Geospatial analyses and visualizations, Applied statistics and Bayesian inference, Machine learning algorithms, Creating machine learning models on geospatial data, Geostatistics, Image processing or computer vision, Distributed computing applications for machine learning and other computations","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":14000,"maxValue":240000,"unitText":"YEAR"}}}]}