Interns will work on one of many computer science-based projects which integrate formulation science, material informatics, cheminformatics, and automation with software engineering, information science, machine learning, large language models (LLM) database building and visualization, statistics, and/or mathematical modeling.
Responsibilities
Engage with the business and scientists to gather requirements, understand use cases, and identify opportunities to improve internal tools, workflows, and business processes
Explore and implement AI, machine learning, analytics, and automation capabilities to improve efficiency, recommend process improvements, and support data-driven decision-making
Develop AI-enabled solutions that use historical data and patterns to recommend templates, workflows, task structures, dependencies, or other ways to manage work more effectively
Integrate and analyze data from multiple internal sources to improve data quality, reporting, dashboards, chatbot capabilities, business insights, and connections across project activities, milestones, and deliverables
Develop and enhance internal tools through workflow automation, responsibility or role mapping, administrative process improvements, and functionality that reduces manual effort
Requirements
Bachelor's degree in Computer Science, Data Science, Information Science, Machine Learning, Statistics, or Automation
Master's degree in Computer Science, Data Science, Information Science, Machine Learning, Statistics, or Automation
Doctoral degree in Computer Science, Data Science, Information Science, Machine Learning, Statistics, or Automation
Bachelor's degree in Chemistry, Materials Science, Chemical Engineering or similar science fields with relevant data or computer science skills
Interns will work on one of many computer science-based projects which integrate formulation science, material informatics, cheminformatics, and automation with software engineering, information science, machine learning, large language models (LLM) database building and visualization, statistics, and/or mathematical modeling.
Responsibilities
Engage with the business and scientists to gather requirements, understand use cases, and identify opportunities to improve internal tools, workflows, and business processes.
Explore and implement AI, machine learning, analytics, and automation capabilities to improve efficiency, recommend process improvements, and support data-driven decision-making.
Develop AI-enabled solutions that use historical data and patterns to recommend templates, workflows, task structures, dependencies, or other ways to manage work more effectively.
Integrate and analyze data from multiple internal sources to improve data quality, reporting, dashboards, chatbot capabilities, business insights, and connections across project activities, milestones, and deliverables.
Develop and enhance internal tools through workflow automation, responsibility or role mapping, administrative process improvements, and functionality that reduces manual effort.
Requirements
Currently pursuing a bachelor’s, master’s, or doctoral degree in Computer Science, Data Science, Information Science, Machine Learning, Statistics, or Automation
Chemistry, Materials Science, Chemical Engineering or similar science fields with relevant data or computer science skills
Must have successfully completed two full years of undergraduate-level Computer Science or Data Science curriculum including associated laboratory courses
