Data Scientist
About The Position
SirenOpt is seeking a Data Scientist to join their Applications Engineering team. This role involves building and deploying machine learning models to transform complex sensor signals into actionable predictions about material properties, thereby connecting raw instrument data with manufacturing intelligence. It's a customer-facing position where you'll work directly with customer samples and datasets to conduct proof-of-concept studies, validate model performance on new materials, and translate findings into product enhancements. Collaboration with software and hardware engineering teams is key to moving models from research to production. The company, SirenOpt, focuses on helping manufacturers improve the quality, safety, and reliability of micro- and nano-materials through a manufacturing intelligence platform that uses plasma, physics-informed machine learning, and predictive analytics to provide real-time material insights.
Requirements
- 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
Nice To Haves
- Experience working with time-series, spectroscopic, or other sensor-based signal data
- Prior work in manufacturing, materials science, energy storage, semiconductors, or another physical science domain
- Prior customer-facing or applications engineering experience in a technical product company
- Experience deploying models in production software environments
- Familiarity with data pipeline development (PostgreSQL or similar)
- Fluency in Mandarin Chinese, Japanese, German, Korean, or another key stakeholder language
Responsibilities
- Build, calibrate, and validate predictive models that map sensor signal features to material properties.
- Design and evaluate new model architectures and featurization strategies suited to small-data, high-dimensional scientific datasets.
- Apply methods including regression, dimensionality reduction, probabilistic modeling, anomaly detection, and physics-informed ML.
- Develop testing and validation frameworks for model performance, including uncertainty quantification and out-of-distribution detection.
- Characterize model robustness across sample types, process conditions, and instrument configurations.
- Prepare models and documentation for handoff to the software engineering team for production deployment.
- Analyze datasets from customer proof of concepts.
- Compile technical reports and supporting materials to deliver to customers.
- Translate findings and stakeholder feedback into model improvement roadmaps.
Benefits
- Equity and Salary compensation depends on experience
- Health, Dental, Vision plans provided
- 401k matching provided
- 20 days of PTO per year
- approximately 15 paid US holidays per year
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
