TDS Digital Ops & Strat Intern

Purpose

The TDS Digital Operations & Strategy organization is seeking a highly motivated Co-Op interested in bioprocess advanced analytics, data science, and Process Analytical Technology (PAT). In this role, the Intern will work at the intersection of bioprocessing, analytical technologies, and data science to support the development of advanced analytical and modeling approaches for bioproduction.

Responsibilities

• Collaborating with cross-functional teams to prepare, test, and execute Design of Experiments (DoE) sample sets involving complex biological matrices.
• Acquiring and analyzing data generated from process sensors and analytical instrumentation, including Raman, IR, and NIR spectroscopy.
• Developing and applying multivariate models using chemometric, statistical, and advanced data science approaches to improve bioprocess understanding.
• Supporting the development, optimization, testing, and validation of analytical and predictive models.
• Applying programming and data science skills to model building, analytical tool development, data processing, and visualization.
• Supporting the integration of in situ analytical approaches and Process Analytical Technology (PAT) into operational workflows.
• Applying statistical analyses to experimental and process datasets to identify trends, relationships, and actionable insights.
• Gaining hands-on experience with PAT instrumentation and data science tools used in bioprocess development.
• Supporting analytical activities involving spectroscopy, chromatography, mass spectrometry, and related technologies, as applicable.
• Documenting analytical methods, models, technical findings, and project outcomes.
• Communicating results and insights to internal research and cross-functional teams.
• Providing additional analytical, digital, and project support as needed.

Qualifications / Requirements

• Completion of Undergraduate Freshman year at an accredited University is required.
• Currently pursuing a Bachelor's, Master's, or PhD degree in Chemical Engineering, Biological Engineering, Biomedical Engineering, Biology, Chemistry, Analytical Chemistry, Biochemistry, Molecular Biology, Microbiology, Pharmaceutical Sciences, Data Science or related scientific, engineering, or quantitative disciplines.
• Have a cumulative GPA of 3.0 or higher, which is reflective of all college coursework.
• Programming experience, including Python coding and experience supporting software development, model building, and/or analytical tool development.
• Strong analytical, quantitative, statistical, and problem-solving skills.
• Ability to analyze and interpret complex scientific or process datasets.
• Ability to work independently as a self-starter while collaborating effectively within multidisciplinary teams.
• Ability to manage multiple projects and priorities while delivering results.
• Permanently authorized to work in the U.S., must not require sponsorship of an employment visa (e.g., H-1B or green card) at the time of application or in the future.

Preferred Qualifications

• Experience with Python and familiarity with R and/or MATLAB.
• Experience developing analytical, statistical, predictive, or multivariate models.
• Experience with chemometric modeling, multivariate data analysis, or related advanced analytics approaches.
• Exposure to deep learning or other advanced modeling methodologies.
• Experience with scientific data processing, visualization, and model validation.
• Interest or experience in software or analytical tool development.
• Experience or familiarity with spectroscopy, including Raman, IR, and/or NIR.
• Experience or familiarity with chromatography and/or mass spectrometry (MS).
• Familiarity with Process Analytical Technology (PAT) instruments, process sensors, or in situ analytical approaches.
• Knowledge of or experience with cell culture is preferred.
• Experience with biological matrices, bioprocessing, or bioproduction is a plus.
• Experience with Design of Experiments (DoE), experimental execution, and statistical analysis.
• Prior laboratory or scientific research experience is preferred.
• Strong communication and presentation skills with the ability to communicate technical findings to multidisciplinary audiences.
• Demonstrated initiative, curiosity, leadership, and collaborative problem-solving skills.