{"schemaVersion":"jobsearcher.job.v1","id":"659dd373c8c4884b5fc8e62a","url":"https://jobsearcher.com/jobs/659dd373c8c4884b5fc8e62a","canonicalUrl":"https://jobsearcher.com/jobs/659dd373c8c4884b5fc8e62a","title":"Data Scientist","description":"About the CompanyVoltForce is working with an early-stage materials-inspection company on the search for a Data Scientist. Our client helps manufacturers make better, safer, and more reliable micro- and nano-scale materials, the building blocks of critical sectors like energy storage, semiconductors, aerospace, and power systems. As demand surges and production grows more complex and faster, small undetected variations during manufacturing lead to waste, lower performance, higher cost, and safety risk, leaving manufacturers effectively flying blind.The company's platform changes that. It scans materials without damaging them, pairing a novel sensing approach with physics-informed machine learning to capture real-time signals that conventional methods cannot access. Those signals give manufacturers rare visibility into how materials behave as they are made.The company turns that hidden data into actionable intelligence, helping manufacturers reduce variability and increase yield and performance. The platform can run as a standalone tool or integrate directly into production lines, and it is already in early deployment with leading global manufacturers.About the RoleWe are seeking a Data Scientist to join the Applications Engineering team. In this role, you will build and deploy machine learning models that turn complex, high-dimensional sensor signals into actionable predictions about material properties, bridging the gap between raw instrument data and the intelligence manufacturers act on.This is a forward-deployed, customer-adjacent role. You will work directly with customer samples and datasets to execute proof-of-concept studies, validate model performance on novel materials, and translate results into product improvements. You will collaborate closely with software and hardware engineering teams to move models from research into production.ResponsibilitiesModel Development & CalibrationBuild, calibrate, and validate predictive models that map sensor signal features to material propertiesDesign and evaluate new model architectures and featurization strategies suited to small-data, high-dimensional scientific datasetsApply methods including regression, dimensionality reduction, probabilistic modeling, anomaly detection, and physics-informed MLModel Validation & Production ReadinessDevelop testing and validation frameworks for model performance, including uncertainty quantification and out-of-distribution detectionCharacterize model robustness across sample types, process conditions, and instrument configurationsPrepare models and documentation for handoff to the software engineering team for production deploymentCustomer-Facing Proof-of-Concept WorkAnalyze datasets from customer proof of conceptsCompile technical reports and supporting materials to deliver to customersTranslate findings and stakeholder feedback into model improvement roadmapsQualificationsRequiredB.S. in Data Science, Statistics, Applied Mathematics, or a related quantitative field with 3 to 5 years of applied ML/data science experience; or M.S. with 1 to 3 years (Ph.D. a plus, not required)Hands-on experience building and validating predictive models (supervised and self-supervised) in PythonAbility to analyze multivariate, high-dimensional datasets and perform feature engineering and selectionSolid grasp of statistical modeling: uncertainty quantification, regularization, covariate analysis, and feature importance methodsStrong communicator, comfortable presenting technical findings to both technical and non-technical audiencesPreferredExperience working with time-series, spectroscopic, or other sensor-based signal dataPrior work in manufacturing, materials science, energy storage, semiconductors, or another physical science domainPrior customer-facing or applications engineering experience in a technical product companyExperience deploying models in production software environmentsFamiliarity with data pipeline development (PostgreSQL or similar)Fluency in Mandarin Chinese, Japanese, German, Korean, or another key stakeholder languageIt's okay if you don't check every box. We hire for curiosity, rigor, and the drive to learn.Equal Opportunity StatementWe are committed to diversity and inclusivity.","company":"Voltforce","rawCompany":"voltforce","city":"Menlo Park","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-07-31T10:49:55.187Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"19-2032.00","title":"Materials Scientists","slug":"materials-scientists"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"}],"industries":[{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"},{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"},{"code":"541330","title":"Engineering Services","slug":"engineering-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Data Scientist","description":"About the CompanyVoltForce is working with an early-stage materials-inspection company on the search for a Data Scientist. Our client helps manufacturers make better, safer, and more reliable micro- and nano-scale materials, the building blocks of critical sectors like energy storage, semiconductors, aerospace, and power systems. As demand surges and production grows more complex and faster, small undetected variations during manufacturing lead to waste, lower performance, higher cost, and safety risk, leaving manufacturers effectively flying blind.The company's platform changes that. It scans materials without damaging them, pairing a novel sensing approach with physics-informed machine learning to capture real-time signals that conventional methods cannot access. Those signals give manufacturers rare visibility into how materials behave as they are made.The company turns that hidden data into actionable intelligence, helping manufacturers reduce variability and increase yield and performance. The platform can run as a standalone tool or integrate directly into production lines, and it is already in early deployment with leading global manufacturers.About the RoleWe are seeking a Data Scientist to join the Applications Engineering team. In this role, you will build and deploy machine learning models that turn complex, high-dimensional sensor signals into actionable predictions about material properties, bridging the gap between raw instrument data and the intelligence manufacturers act on.This is a forward-deployed, customer-adjacent role. You will work directly with customer samples and datasets to execute proof-of-concept studies, validate model performance on novel materials, and translate results into product improvements. You will collaborate closely with software and hardware engineering teams to move models from research into production.ResponsibilitiesModel Development & CalibrationBuild, calibrate, and validate predictive models that map sensor signal features to material propertiesDesign and evaluate new model architectures and featurization strategies suited to small-data, high-dimensional scientific datasetsApply methods including regression, dimensionality reduction, probabilistic modeling, anomaly detection, and physics-informed MLModel Validation & Production ReadinessDevelop testing and validation frameworks for model performance, including uncertainty quantification and out-of-distribution detectionCharacterize model robustness across sample types, process conditions, and instrument configurationsPrepare models and documentation for handoff to the software engineering team for production deploymentCustomer-Facing Proof-of-Concept WorkAnalyze datasets from customer proof of conceptsCompile technical reports and supporting materials to deliver to customersTranslate findings and stakeholder feedback into model improvement roadmapsQualificationsRequiredB.S. in Data Science, Statistics, Applied Mathematics, or a related quantitative field with 3 to 5 years of applied ML/data science experience; or M.S. with 1 to 3 years (Ph.D. a plus, not required)Hands-on experience building and validating predictive models (supervised and self-supervised) in PythonAbility to analyze multivariate, high-dimensional datasets and perform feature engineering and selectionSolid grasp of statistical modeling: uncertainty quantification, regularization, covariate analysis, and feature importance methodsStrong communicator, comfortable presenting technical findings to both technical and non-technical audiencesPreferredExperience working with time-series, spectroscopic, or other sensor-based signal dataPrior work in manufacturing, materials science, energy storage, semiconductors, or another physical science domainPrior customer-facing or applications engineering experience in a technical product companyExperience deploying models in production software environmentsFamiliarity with data pipeline development (PostgreSQL or similar)Fluency in Mandarin Chinese, Japanese, German, Korean, or another key stakeholder languageIt's okay if you don't check every box. We hire for curiosity, rigor, and the drive to learn.Equal Opportunity StatementWe are committed to diversity and inclusivity.","datePosted":"2026-07-31T10:49:55.187Z","dateModified":"2026-07-31T10:49:55.187Z","hiringOrganization":{"@type":"Organization","name":"Voltforce","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Menlo Park","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"659dd373c8c4884b5fc8e62a"},"url":"https://jobsearcher.com/jobs/659dd373c8c4884b5fc8e62a"}}