D. E. Shaw

TDS Digital Ops & Strat Co-Op

PA
  • On-site
  • Full-time

Listing details

  • Closes Nov 8
  • Posted Oct 9
  • Last verified Oct 9

About the job

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity.

Learn more at jnj.com. As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual.

At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit. Job Function: Career Programs Job Sub Function: Non-LDP Intern/Co-Op Job Category: Career Program All Job Posting Locations: Malvern, Pennsylvania, United States of America Job Description: About Innovative Medicine Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way. Learn more at https://www.jnj.com/innovative-medicine We are searching for the best talent for TDS Digital Ops & Strat Co-Op to be in Malvern, PA. 1 vacancy is currently available. The Co-op term is six months typically from January to June, 2027 (flexible).

Full time requirement (40 hours per week) Fully On-site. 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 Co-Op will work at the intersection of bioprocessing, analytical technologies, and data science to support the development of advanced analytical and modeling approaches for bioproduction.

The position provides hands-on experience with process sensors, spectroscopy, multivariate modeling, programming, model development, and analytical tools while collaborating with cross-functional scientific and technical teams. You will be responsible: 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.

You must not have any Summer Internship contract or other employment commitment that could interfere with the contract dates specified for this position. 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. Students currently on CPT, OPT, or STEM OPT usually requires future sponsorship for long term employment and do not meet the requirements for this program unless eligible for an alternative long-term status that does not require company sponsorship. If you are under 18 years of age, you (the candidate) may need to obtain the necessary working papers or other documentation required by state law to start the assignment, as well as get a parent’s consent for the background check.

Preferred Qualifications: Programming, Data Science & Modeling: 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 p