AI Trainer - Chemistry

Planet Pharma; United States; On-site; USD 60,000 - 90,000; Full time

Job summary

Planet Pharma is seeking candidates with 2+ years of applied experience in core sciences or engineering or data science. The role emphasizes coding, data analysis, and hands-on work with real data, instruments, and analysis tools (MATLAB, Python or R). A progress toward a bachelor’s degree is preferred, with emphasis on statistical thinking and experimental design.

Comfort with command line and evaluating cross-disciplinary work will be valued as projects vary across data-rich environments.

Qualifications

• 2+ years of applied experience preferred in core sciences or in engineering or data science.
• Coding / scientific computing with Python or similar and command line proficiency.
• Hands-on with real data and analysis tools (MATLAB, Python or R; SPICE and PCB tools; FEA or CFD; GIS; spectroscopy software).
• Comfort with statistical and experimental reasoning: experiment design, measurement error and uncertainty, and common statistical traps.

Skills

Python

Data analysis

MATLAB

R

Statistics

Education

Bachelor’s degree in progress

Tools

SPICE

PCB tools

FEA

CFD

GIS

spectroscopy software

Job description

2+ years of applied experience preferred in one of the core sciences (mathematics, physics, chemistry, or biology) or in an engineering discipline (electrical and electronics, civil and structural, materials, chemical and process, environmental and earth sciences, or similar) or in data science.

In progress Bachelor’s degree or higher. We prefer 2+ years of experience outside undergraduate study.

Coding / scientific computing is a strong plus and is required on some projects: comfortable writing and verifying work in code (Python or similar) and working at the command line.

Hands-on with real data and the tools of your field: instrument and test data, measurement files, simulation outputs, schematics or drawings, and the analysis tools that go with them (MATLAB, Python or R; SPICE and PCB tools; FEA or CFD; GIS; chromatography or spectroscopy software).

Comfort with statistical and experimental reasoning: experiment design, measurement error and uncertainty, and the common statistical traps. The assessment leans on this.

Working understanding of adjacent sub-disciplines, enough to assess work outside your own specialty and point out what was done correctly or incorrectly.