SirenOpt is accelerating sustainable and smart manufacturing of advanced materials using intelligent characterization and real-time decision-making to drive the creation of innovative, high-performance products that fuel the advancement of society. Backed by Activate Venture Partners.
About the role
Job Title: Metrology Data Scientist – Signal Modeling & Applied Metrology We are seeking a Metrology 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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- B.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)
- Hands-on experience building and validating predictive models (supervised and self-supervised) in Python
- Ability to analyze multivariate, high-dimensional datasets and perform feature engineering and selection
- Solid grasp of statistical modeling: uncertainty quantification, regularization, covariate analysis, and feature importance methods
- Strong communicator, comfortable presenting technical findings to both technical and non-technical audiences
More about this role
At SirenOpt®, we are building the manufacturing intelligence layer for advanced materials. We help manufacturers visualize, understand, and control the material behavior that determines yield, performance, and safety across energy, aerospace, automotive, electronics, and semiconductor applications.
We are working on this problem because advanced materials are becoming more critical and more complex, but the tools used to manufacture them have not kept pace. Manufacturers still lack real-time visibility into the small variations that shape quality on the line. We care because those invisible variations cause scrap, waste energy and materials, create higher costs, inconsistent product performance, and introduce avoidable safety risks.
We developed PlasmaSens™, our manufacturing intelligence platform, to close that visibility gap and to lay the foundation for truly autonomous manufacturing.
We are uniquely suited to solve this problem because our expertise and our platform connect material science, plasma physics, machine learning, and manufacturing in one system. The same core technology works in-line, at-line, and off-line, and across form factors ranging from electrodes and wafers...
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