Data Scientist at Covalent in Sunnyvale, CA

Why Join Covalent

At Covalent, you’ll work alongside world-class scientists and engineers in a dynamic, collaborative environment. We empower our team members to take ownership of their work, innovate constantly, and engage directly with customers, shaping the future of technology.

Role Overview

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.

Key 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.

Preferred 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