{"id":1675714,"url":"https://alion.io/job/sirenopt-data-scientist","title":"Data Scientist","company":{"id":687018,"name":"SirenOpt","domain":"sirenopt.com","url":"https://alion.io/company/sirenopt","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Leandro, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":100000,"max":160000,"currency":"USD","period":"year","gross":null,"usd_annual":160000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Self-Supervised Learning","optional":false},{"name":"PostgreSQL","optional":true},{"name":"Time Series Forecasting","optional":true}],"status":"live","first_seen_at":"2026-10-01T23:01:46Z","employer_posted_date":"2026-10-01","last_verified_at":"2026-10-06T20:22:28Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"About SirenOpt\nSirenOpt helps manufacturers make better, safer, and more reliable micro- and nano-materials. These materials are the building blocks of critical sectors of the global economy such as batteries, computer chips, aircraft components, and power systems. But, surging material demand and growing complexity are pushing production to unprecedented scales and speeds, leaving manufacturers effectively flying blind. Small, undetected variations during production lead to wastage, lower performance, higher costs, and safety risks.\nSirenOpt is changing this by developing a manufacturing intelligence platform that non-destructively probes materials during production, revealing critical internal information without damaging them. Using a novel combination of cold atmospheric plasma, physics-informed machine learning, and predictive analytics, SirenOpt generates unique, real-time material fingerprints that capture material signals not accessible through conventional measurement techniques. These insights give manufacturers unprecedented visibility into how materials behave as they are made.\nWe turn hidden data into actionable intelligence to help manufacturers reduce variability and thus increase yield and performance. The technology can be deployed as a standalone tool or integrated directly into factory production lines. SirenOpt is currently deploying early versions of its platform with some of the largest industrial manufacturers in the world across North America, Europe, and Asia.\nAbout the job\nJob Title: Data Scientist - Signal Modeling & Applied Metrology\nLocation: On-Site (San Leandro, CA)\nJob Type: Full-Time\nRole Overview\nWe are seeking a Data Scientist to join our 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 manufacturing intelligence.\nThis 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.\nWhat You'll Do\nModel Development & Calibration\nBuild, calibrate, and validate predictive models that map sensor signal features to material properties\n\nDesign and evaluate new model architectures and featurization strategies suited to small-data, high-dimensional scientific datasets\n\nApply methods including regression, dimensionality reduction, probabilistic modeling, anomaly detection, and physics-informed ML\n\nModel Validation & Production Readiness\nDevelop testing and validation frameworks for model performance, including uncertainty quantification and out-of-distribution detection\n\nCharacterize model robustness across sample types, process conditions, and instrument configurations\n\nPrepare models and documentation for handoff to the software engineering team for production deployment\n\nCustomer-Facing Proof-of-Concept Work\nAnalyze datasets from customer proof of concepts\n\nCompile technical reports and supporting materials to deliver to customers\n\nTranslate findings and stakeholder feedback into model improvement roadmaps\n\nWhat We're Looking For\nB.S. in Data Science, Statistics, Applied Mathematics, or a related quantitative science field with 3-5 years of applied ML/data science experience; or M.S. with 1-3 years (Ph.D. a plus, not required)\n\nHands-on experience building and validating predictive models (supervised and self-supervised) in Python\n\nAbility to analyze multivariate, high-dimensional datasets and perform feature engineering and selection\n\nSolid grasp of statistical modeling: uncertainty quantification, regularization, covariate analysis, and feature importance methods\n\nStrong communicator; comfortable presenting technical findings to both technical and non-technical audiences\n\nNice to have:\nExperience working with time-series, spectroscopic, or other sensor-based signal data\n\nPrior work in manufacturing, materials science, energy storage, semiconductors, or another physical science domain\n\nPrior customer-facing or applications engineering experience in a technical product company\n\nExperience deploying models in production software environments\n\nFamiliarity with data pipeline development (PostgreSQL or similar)\n\nFluency in Mandarin Chinese, Japanese, German, Korean, or another key stakeholder language\n\nBase Pay Range\n$100,000—$160,000 USD\nBenefits\nEquity and Salary compensation depends on experience \nHealth, Dental, Vision plans provided\n401k matching provided\nTime off: 20 days of PTO per year, plus approximately 15 paid US holidays per year","description_format":"text","description_chars":4841,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[{"language":"Korean","level":"All levels","optional":true},{"language":"Chinese","level":"All levels","optional":true}]},"benefits":["401k plan","Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Manufacturing","Advanced Materials","Electrical & Electronic Engineering"],"lifecycle":[{"event":"open","at":"2026-10-02T08:27:44Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 3","filings_12m":3,"filings_prev_12m":0,"green_card_filings_12m":0,"median_offered_wage_usd":115833,"route":null,"cap_exempt":false,"checked_at":"2026-10-03T21:08:04+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)"],"filings_for_role_12m":0}],"liveness":{"score":89,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.893,"p_room":1,"age_days":4,"expected_fill_days":34,"reasons":["conf:1","velocity","win:early"],"computed_at":"2026-10-06T05:45:30Z"},"pay":{"stated_usd_annual":160000,"is_top_pay":true},"html_url":"https://alion.io/job/sirenopt-data-scientist","json_url":"https://alion.io/job/sirenopt-data-scientist.json","meta":{"generated_at":"2026-10-06T22:54:44Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":3692,"day_limit":5000,"remaining_today":1308,"minute_limit":60,"resets_at":"2026-10-07T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":687018},"rest":"https://alion.io/mcp/rest/get_company?id=687018"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fsirenopt-data-scientist"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fsirenopt-data-scientist"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fsirenopt-data-scientist"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/sirenopt-data-scientist\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fsirenopt-data-scientist"}]}