> Markdown version of [/jobs/ext/1251178-data-scientist](https://www.wearedevelopers.com/jobs/ext/1251178-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:** Stanford Blood Center, LLC
- **Location:** Palo Alto, CA, United States
- **Experience:** Experienced
- **Salary:** $156,000.0 - $208,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Extract Transform Load (ETL), Data Warehousing, Python (Programming Language), Machine Learning, Language Modeling, Software Engineering, Large Language Models, Prompt Engineering, Information Technology, Data Pipelines
- **Published:** July 12, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=2fb983c498758e38

## About the Role

* A Bachelor's in computer science, data science, engineering, or related field (+4 years experience), or a Master's in a related field.
 * 3-5+ years building production ML/AI systems, ideally with real ownership of a data warehouse or complex ETL environment.
 * Strong Python chops and genuine software engineering skill - not just modeling, but building things that run reliably in production.
 * Hands-on LLM experience: fine-tuning, prompt engineering, deploying language models in real applications.
 * Comfort moving between deep technical work and stakeholder conversations - you can explain a forecast to a clinician as easily as you can tune a query.

## Description

Most data scientists build models that optimize ad clicks or shopping carts. At Stanford Blood Center, your models help decide how blood gets to the patients who need it. If you want your AI and ML work to matter in a way you can actually see, keep reading.

We're hiring a Data Scientist to build predictive models, deploy LLM-powered tools, and architect the data pipelines that power blood supply forecasting, resource optimization, and clinical decision support at Stanford Blood Center.

The Role, in Short

 * Design and ship AI/ML models - from LLM fine-tuning and prompt engineering to predictive forecasting - that directly influence clinical and operational decisions.
 * Own the data: build and optimize the ETL pipelines and EDW architecture your models (and everyone else's dashboards) run on.
 * Build real things people use - applications and tools, not just notebooks - using Python and solid software engineering practice.
 * Work shoulder-to-shoulder with clinical and operational teams, and see the impact of your work in weeks, not quarters., You'll have real latitude here - this is a team building its data science practice, not maintaining someone else's roadmap. Your work directly supports a safe, sufficient blood supply for patients across the region while supporting the advancement of transfusion and transplantation medicine at Stanford. And as the practice grows, you'll have the chance to mentor others.


## Related Videos

- [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology)
- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning)
- [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)
- [Python-Based Data Streaming Pipelines Within Minutes](https://www.wearedevelopers.com/videos/1233-python-based-data-streaming-pipelines-within-minutes)
- [Analytics in the Age of Agentic AI: A tour of ClickHouse and Langfuse](https://www.wearedevelopers.com/videos/100240-analytics-in-the-age-of-agentic-ai-a-tour-of-clickhouse-and-langfuse)

## Related Articles

- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)
- [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)
- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)