R&D Commercial Product Development Engineer Intern

LPU Batangas; United States; Sur place; USD 23 000 - 32 000; Temps partiel

Avantages offerts par ce poste

Internal training programs

Employee Resource Groups promoting DEI

Résumé du poste

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

Responsabilités

• 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.

Connaissances

Python programming

Statistical DOE concepts

Strong communication

Data interpretation

Formation

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

Outils

Python

DOE tools

Google Workspace

LIMS/ELN

Description du poste

Your Role

Here’s what you will be doing:

• Extract, clean, and standardize complex historical formulation datasets from legacy spreadsheets, LIMS, and experimental records.
• Perform automated statistical analyses to uncover critical ingredient-property relationships.
• Develop analytical summaries and interactive dashboards to present insights to R&D stakeholders.
• Train, evaluate, and fine-tune supervised machine learning models to predict physical, mechanical, and rheological material properties.
• Apply inverse prediction algorithms, optimization techniques, and grid searches to identify candidate formulation recipes.
• Design multi-run DOE test protocols balancing data exploration with laboratory validation.
• Assist in configuring LIMS templates and structured ELN procedures to streamline benchtop data capture.
• Standardize end-to-end work orders connecting raw material formulations, processing steps, and characterization results into a seamless data pipeline.
• Support digital knowledge management by organizing and integrating technical literature, reports, and standard operating procedures into central repositories.

About You

The company is looking for:

• Currently pursuing a Bachelor's degree in Chemistry, Chemical Engineering, Materials Science, or a related scientific/engineering discipline.
• Proficiency with Python, statistical DOE tools, Google Workspace, and exposure to generative AI platforms.
• Solid foundational knowledge of general chemistry, material science, polymer chemistry, formulation principles, and analytical characterization.
• Strong interpersonal and communication skills with the ability to translate complex data science concepts into clear insights for experimental scientists and leadership.

Preferred qualifications include hands‑on experience with LIMS or Electronic Lab Notebook platforms, familiarity with applied machine learning in chemistry or materials science (e.g., ensemble modeling, multi‑objective optimization, transfer learning), practical experience constructing automated solution pipelines, and prior wet lab or industrial chemistry laboratory experience.

Compensation & Benefits

• Base hourly rate of $20.00.
• Internal training programs and courses for professional growth and development.
• Employee Resource Groups promoting diversity and inclusion.