Data Scientist
About this role
Translating complex, high-dimensional sensor signals into actionable predictions about material properties forms the core daily work for this position within the Applications Engineering team. Building and deploying predictive machine learning models that bridge raw instrument data and manufacturing intelligence occupies a significant portion of the schedule. Direct collaboration with customer samples involves executing proof-of-concept studies, validating performance on novel materials, and translating results into product improvements alongside software and hardware engineering groups.
SirenOpt develops a manufacturing intelligence platform combining cold atmospheric plasma, physics-informed machine learning, and predictive analytics to generate real-time material fingerprints. The role exists to support early platform deployments with large industrial manufacturers across global markets by turning hidden data into intelligence that reduces production variability. Candidates who combine quantitative scientific training with practical modeling experience and strong customer communication skills will thrive in this environment.
What you will do
- 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 machine learning.
- 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 and compile technical reports to deliver to clients.
What you need
- Hold a Bachelor of Science degree in Data Science, Statistics, Applied Mathematics, or a related quantitative field with three to five years of applied machine learning experience, or a Master of Science with one to three years.
- Demonstrate hands-on experience building and validating supervised and self-supervised predictive models using Python.
- Possess the ability to analyze multivariate, high-dimensional datasets while performing feature engineering and selection.
- Maintain a solid grasp of statistical modeling concepts such as uncertainty quantification, regularization, covariate analysis, and feature importance methods.
- Communicate clearly and comfortably when presenting technical findings to both technical and non-technical audiences.
Nice to have
- 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 including PostgreSQL or similar tools
- Fluency in Mandarin Chinese, Japanese, German, Korean, or another key stakeholder language
Benefits
- Equity and salary compensation depending on experience
- Health, dental, and vision plans provided
- 401k matching provided
- 20 days of paid time off per year
- Approximately 15 paid US holidays per year
Skills and tech stack
Questions candidates ask
Where is this job located?
This role is on-site at the company location in San Leandro, California.
What is the salary range for this position?
The base pay range is $100,000 to $160,000 USD, with equity and salary compensation depending on experience.
What kind of team will I be working with?
You will join the Applications Engineering team and collaborate closely with software and hardware engineering teams.
What time off is included with the position?
Employees receive 20 days of PTO per year, plus approximately 15 paid US holidays per year.