> Markdown version of [/jobs/ext/3571873-data-scientist](https://www.wearedevelopers.com/jobs/ext/3571873-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Scientist

- **Company:** SirenOpt Inc
- **Location:** San Leandro, CA, United States
- **Experience:** Experienced
- **Salary:** $100,000.0 - $160,000.0
- **Contract:** Permanent contract
- **Skills:** Computer Engineering, Python (Programming Language), PostgreSQL, Machine Learning, Software Engineering, Feature Engineering, Machine Learning Operations, Data Pipelines
- **Published:** October 3, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=7766d3612aeb2756

## About the Role

* 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 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 (PostgreSQL or similar)
 * Fluency in Mandarin Chinese, Japanese, German, Korean, or another key stakeholder language

## Description

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

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


## Related Videos

- [Data Science, ML & AI in the Oil and Gas Industry at NDT Global - Dr. Katja Träumner](https://www.wearedevelopers.com/videos/1308-data-science-ml-ai-in-the-oil-and-gas-industry-at-ndt-global-dr-katja-traumner)
- [Optimizing Discovery: PostgreSQL's Role in Transforming GetYourGuide's Search](https://www.wearedevelopers.com/videos/1647-optimizing-discovery-postgresql-s-role-in-transforming-getyourguide-s-search)
- [Why and when should we consider Stream Processing frameworks in our solutions](https://www.wearedevelopers.com/videos/1085-why-and-when-should-we-consider-stream-processing-frameworks-in-our-solutions)
- [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning)
- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [What non-automotive Machine Learning projects can learn from automotive Machine Learning projects](https://www.wearedevelopers.com/videos/397-what-non-automotive-machine-learning-projects-can-learn-from-automotive-machine-learning-projects)

## Related Articles

- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again)
- [Is Software Engineering Over-Saturated?](https://www.wearedevelopers.com/magazine/418-is-software-engineering-over-saturated)
- [The Biggest German Tech Companies](https://www.wearedevelopers.com/magazine/424-the-biggest-german-tech-companies)