Data Scientist: Signal Modeling & Applied Metrology

Activate; San Leandro (CA); On-site; USD 100,000 - 160,000; Full time

Benefits offered by this job

Equity

Health/Dental/Vision

401k matching

PTO

Job summary

SirenOpt is seeking a Data Scientist to join its Applications Engineering team on-site in San Leandro, CA. You will build and deploy ML models that turn high-dimensional sensor signals into actionable predictions about material properties, bridging raw instrument data and manufacturing intelligence.

This forward-deployed, customer-adjacent role involves proof-of-concept studies and close collaboration with software and hardware engineers to move models into production and translate results into

Qualifications

• B.S. in Data Science, Statistics, Applied Mathematics, or related field with 3–5 years of ML experience; or M.S. with 1–3 years (Ph.D. a plus).
• Hands-on experience building and validating predictive models in Python.
• Ability to analyze high-dimensional datasets and perform feature engineering.
• Solid grasp of statistical modeling: uncertainty quantification, regularization, covariate analysis.

Responsibilities

• Model development, calibration, and validation of predictive models mapping sensor signals to material properties.
• Design and evaluate new architectures and featurization strategies for small-data, high-dimensional data.
• Apply regression, dimensionality reduction, probabilistic modeling, anomaly detection, and physics-informed ML.
• Develop testing and validation frameworks including uncertainty quantification and OOD detection.
• Prepare models and docs for handoff to software engineering for production deployment.

Skills

Predictive modeling

Python ML

Multivariate analysis

Uncertainty quantification

Education

B.S. in Data Science / Statistics / Applied Math

M.S. in related field

Tools

Python

Pandas / NumPy

SciKit-Learn

Job description

SirenOpt is seeking a Data Scientist to join its Applications Engineering team on-site in San Leandro, CA. You will build and deploy ML models that turn high-dimensional sensor signals into actionable predictions about material properties, bridging raw instrument data and manufacturing intelligence.

This forward-deployed, customer-adjacent role involves proof-of-concept studies and close collaboration with software and hardware engineers to move models into production and translate results into