R&D Product Development Engineer Intern: Data-Driven Formulations

LPU Batangas; United States; On-site; USD 23,000 - 32,000; Part time

Benefits offered by this job

Internal training programs

Employee Resource Groups promoting DEI

Job summary

LPU Batangas in the United States seeks a motivated student to join the R&D data analytics team as an intern. You will extract and standardize complex datasets from legacy systems and apply DOE-based analyses to reveal ingredient-property relationships.

You will build interactive dashboards, train ML models for material properties, and help optimize experimental formulations while gaining exposure to LIMS/ELN workflows and digital knowledge management.

Qualifications

• Pursuing a Bachelor's degree in a technical field
• Knowledge of Python and statistical DOE methods
• Familiarity with Google Workspace and AI platforms
• Ability to translate data into insights for scientists and leadership

Responsibilities

• Extract, clean, and standardize historical datasets from legacy sources.
• Perform automated analyses to uncover ingredient-property relationships.
• Develop analytical summaries and dashboards for R&D stakeholders.
• Train and fine-tune supervised ML models to predict material properties.
• Apply optimization and inverse-prediction methods to identify formulations.
• Design DOE protocols balancing exploration and validation.
• Assist in configuring LIMS templates and ELN procedures.
• Integrate formulations, processes, and results into data pipelines.
• Support digital knowledge management and central repositories.

Skills

Python programming

Statistical DOE concepts

Strong communication

Data interpretation

Education

Bachelor's degree in Chemistry, Chemical Engineering, Materials Science (in progress)

Tools

Python

DOE tools

Google Workspace

LIMS/ELN

Job description

LPU Batangas in the United States seeks a motivated student to join the R&D data analytics team as an intern. You will extract and standardize complex datasets from legacy systems and apply DOE-based analyses to reveal ingredient-property relationships.

You will build interactive dashboards, train ML models for material properties, and help optimize experimental formulations while gaining exposure to LIMS/ELN workflows and digital knowledge management.