Project Role: Data Scientist – Scientific & Analytic Systems
Location: Sunnyvale, CA (On-site)
Description: We are seeking a Data Scientist to work at the intersection of laboratory science and data-driven software systems. This role is in our software organization and collaborates closely with scientists, engineers, and business stakeholders to translate scientific workflows into productized analytical tools and data-informed systems.
The role combines hands-on scientific data analysis and cross-functional problem solving. You will be expected to engage deeply with how data is generated, interpreted, and used — both in laboratory contexts and in broader business and operational systems that depend on scientific understanding.
This position is well suited for someone with strong analytical instincts, a background in experimental physics, chemistry, or materials science, experience working with experimental or instrument-generated data, and the ability to apply data science techniques in environments where domain context matters as much as algorithms.
Experience: Hands-on data analysis using Python, SQL, or similar tools
Salary: $110,000 – $190,000
Desired Qualifications
We are intentionally flexible on formal credentials. Strong candidates may come from academic research, measurements in technical industries, or applied data science.
You should have experience with:
- Hands-on data analysis using Python, SQL, or similar tools
- Working with experimental, instrument-generated, imaging, or sensor data
- Exploratory data analysis, statistical reasoning, and visualization
- Writing code to process, analyze, or automate data workflows
It’s a plus if you have experience with:
- Machine learning applied to real-world scientific or experimental problems
- Imaging, signal processing, or high-dimensional data
- Cloud-based data tools, databases, or ETL pipelines
- Large language model technologies, or agentic workflow development
Who Will Thrive Here
This role is a strong fit if you:
- Are comfortable taking ownership of ambiguous, domain-heavy problems
- Enjoy working close to real instruments, experiments, and physical systems
- Can move between scientific detail and higher-level system thinking
- Communicate effectively with both scientists and non-technical stakeholders
- Want to apply data science in contexts where correctness, assumptions, and interpretation truly matter
You may have a background in scientific research, applied machine learning, or engineering, and be motivated by roles where scientific understanding is a core part of technical decision-making.
Why Join Us
- Work on data-driven problems rooted in real physical measurement systems
- Influence both scientific workflows and business-facing systems
- Collaborate across laboratory, software, and operations teams
- Tackle problems where domain insight is as important as technical skill
- Grow into deeper technical and domain ownership over time
Employment Type: Full-Time
Roles & Responsibilities
- Design and implement exploratory data analyses, proof-of-concept tools, and applied machine learning solutions to address scientific, operational, and analytical problems.
- Provide scientific and analytical subject matter expertise for data-driven tools that intersect scientific workflows and business processes, ensuring domain assumptions are correctly represented.
- Analyze and interpret data generated by laboratory instruments and measurement workflows, developing analytical methods, models, and visualizations grounded in experimental reality.
- Collaborate with laboratory staff to identify data quality issues, sources of variability, and opportunities for improved measurement, analysis, or automation.
- Contribute to the design and evolution of data pipelines, databases, and structured metadata systems supporting both laboratory and operational data.
- Translate ambiguous scientific and operational questions into well-defined analytical problems and propose data-driven approaches to address them.
- Communicate findings, assumptions, and limitations clearly to scientists, engineers, and non-technical stakeholders.