Data Scientist

Data Scientist – Signal Modeling & Applied Metrology

Location: San Francisco Bay Area (in-office)

Team: Applications Engineering

Volt Force is working with an early-stage materials-inspection company on the search for a Data Scientist. Our client helps manufacturers make better, safer, and more reliable micro- and nano-scale materials, the building blocks of critical sectors like energy storage, semiconductors, aerospace, and power systems. As demand surges and production grows more complex and faster, small undetected variations during manufacturing lead to waste, lower performance, higher cost, and safety risk, leaving manufacturers effectively flying blind.

The company’s platform changes that. It scans materials without damaging them, pairing a novel sensing approach with physics-informed machine learning to capture real-time signals that conventional methods cannot access. Those signals give manufacturers rare visibility into how materials behave as they are made.

The company turns that hidden data into actionable intelligence, helping manufacturers reduce variability and increase yield and performance. The platform can run as a standalone tool or integrate directly into production lines, and it is already in early deployment with leading global manufacturers.

We are seeking a Data Scientist to join the 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 the intelligence manufacturers act on.

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.

• 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

• B.S. in Data Science, Statistics, Applied Mathematics, or a related quantitative field with 3 to 5 years of applied ML/data science experience; or M.S. with 1 to 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

• 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

It's okay if you don't check every box. We hire for curiosity, rigor, and the drive to learn.