Applied Data Scientist - Lab Analytics & Tools

Covalent; Sunnyvale (CA); Ibrido; USD 110.000 - 190.000; Tempo pieno

Descrizione del lavoro

Covalent in Sunnyvale, CA is seeking a Data Scientist to join the software organization and translate scientific workflows into productized analytical tools. You will collaborate with scientists, engineers, and business stakeholders to bring data-driven insights to life.

You will design exploratory analyses, build practical ML solutions, and shape data pipelines while balancing domain understanding with robust analytics.

Competenze

• Background in experimental physics, chemistry, or materials science.
• Experience with experimental or instrument-generated data.
• Ability to apply data science techniques in domain contexts.

Mansioni

• Design and implement exploratory data analyses, PoCs, and ML solutions.
• Provide scientific and analytical SME for data-driven tools intersecting workflows and business processes.
• Analyze data from laboratory instruments and measurement workflows, developing analytic methods and visualizations.
• Collaborate with laboratory staff to identify data quality issues, sources of variability, and automation opportunities.
• Contribute to the design and evolution of data pipelines, databases, and metadata systems.
• Translate ambiguous scientific questions into well-defined analytical problems and propose data-driven approaches.
• Communicate findings and limitations clearly to scientists, engineers, and non-technical stakeholders.

Conoscenze

Python

SQL

Exploratory data analysis

Statistical reasoning

Data visualization

Data workflows

Strumenti

ETL pipelines

Cloud data tools

Descrizione del lavoro

Covalent in Sunnyvale, CA is seeking a Data Scientist to join the software organization and translate scientific workflows into productized analytical tools. You will collaborate with scientists, engineers, and business stakeholders to bring data-driven insights to life.

You will design exploratory analyses, build practical ML solutions, and shape data pipelines while balancing domain understanding with robust analytics.