Data Scientist

Activate; San Leandro (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

SirenOpt is seeking a Data Scientist to join the Applications Engineering team. The role focuses on turning high-dimensional sensor data into actionable material-property predictions, bridging research with production deployment.

You will work forward-deployed with customers, validate models on new materials, and collaborate with software and hardware teams to move models into production.

Qualifications

• 3–5 years of applied ML/data science experience (MS 1–3 years; PhD is a plus)
• Hands-on experience building/validating predictive models in Python
• Ability to analyze high-dimensional datasets and perform feature engineering
• Solid grasp of uncertainty quantification and regularization in modeling
• Strong communication skills, able to present findings to diverse audiences

Responsibilities

• Build, calibrate, and validate predictive models mapping sensor features to material properties
• Develop validation frameworks including uncertainty quantification and out-of-distribution detection
• Prepare models and docs for handoff to software engineering for production
• Analyze customer data and translate findings into model improvement roadmaps

Skills

ML/Data science

Python

Feature engineering

Statistical modeling

Communication

Education

Bachelor's or Master’s in a quantitative field

Tools

PostgreSQL

Job description

Data Scientist – Signal Modeling & Applied Metrology

Company: SirenOpt
Location: San Leandro, CA (In-Office)
Team: Applications Engineering

About SirenOpt

SirenOpt 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.

SirenOpt is changing this. Our manufacturing intelligence platform scans materials, revealing critical internal information without damaging them. Using a novel combination of cold atmospheric plasma, physics-informed machine learning, and predictive analytics, SirenOpt creates unique, real-time fingerprints that capture material signals no other method can access. These insights give manufacturers unprecedented visibility into how materials behave as they are made.

We 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 our platform with some of the largest industrial manufacturers in the world across North America, Europe, and Asia.

About the Role

We 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.

This 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.

What You'll Do

Model Development & Calibration

• 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

Model Validation & Production Readiness

• 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

Customer-Facing Proof-of-Concept Work

• 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

What You Bring

Required

• 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

Preferred

• 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