Data Science Co-Op
Berkeley, CA - USA
Job Summary
Data Science Co-Op | ||
In this coop role you will integrate and develop workflows for data analysis applications evaluate machine learning and advanced statistical methods and explore their applications in the biopharmaceutical industry to support process understanding monitoring and optimization in process development and GMP manufacturing environments.
Co-op Program Dates:
Spring Co-op: January 11 2027 June 11 2027
Fall Co-op: May 17 2027 or June 14 2027 December 10 2027
YOUR TASKS AND RESPONSIBILITIES
The primary responsibilities of this role include:
- Develop test and document software applications and methods to increase the efficiency of advanced data analytics applications in the biotechnology industry;
- Use machine learning and advanced statistical methods to support process understanding monitoring and optimization;
- Develop data analysis applications and workflows for mining preprocessing and visualization of data;
- Develop code in multiple programming languages (e.g. Python R etc.);
- Support mining of process data from IT systems and databases;
- Evaluate and apply different machine learning and advanced statistical methods;
- Interact with data scientists to support activities and projects in the areas of bioprocess monitoring root cause analysis process understanding and process improvements;
- Communicate the outcome of development activities to the team;
- Document project outcomes in a short technical report.
WHO YOU ARE
Bayer seeks an incumbent who possesses the following:
Required Skills and Experience:
- Currently enrolled in a PhD program with a preferred focus in Engineering Computer Science Applied Math or (Bio)Statistics;
- Undergraduate degree in Engineering Computer Science Applied Math or (Bio)Statistics;
- Strong verbal and written communication skills;
- Ability to deal professionally with internal customers of various organizational levels;
- Ability to work effectively within the team and crossfunctionally;
- Good organization documentation prioritization and scheduling skills together with an overall desire to learn;
- Strong problemsolving skills and critical thinking;
- Ability to learn new technical topics;
- Ability to work independently and multitask;
- Interest in machine learning applications (some experience is preferred);
- Programming experience in multiple languages/environments (such as Python R etc.);
- Familiarity or experience with cloud environments (e.g. AWS GCP Azure) preferred.
Employees can expect to be paid an hourly rate of approximately between $22.75 to $45.50. Additional compensation may include a bonus or commission (if relevant). Additional benefits may include health care vision dental retirement PTO sick leave etc (if relevant). This salary (or salary range) is merely an estimate and may vary based on an applicants location market data/ranges an applicants skills and prior relevant experience certain degrees and certifications and other relevant factors.
This posting will be available for application until at least March 19 2027
| YOUR APPLICATION | |
Bayer offers a wide variety of competitive compensation and benefits programs. If you meet the requirements of this unique opportunity and want to impact our mission Health for all Hunger for none we encourage you to apply now. Be part of something bigger. Be you. Be Bayer. | |||
| Equal Opportunity Employer Statement: Notice for U.S. Visitors: All information on this site is subject to compliance with local rule and regulations as they may vary from time to time and across different geographies including without limitation U.S. Executive Orders. | |||
| Bayer is an E-Verify Employer. | |||
| Location: | United States : California : Berkeley | ||
| Division: | Pharmaceuticals | ||
| Reference Code: | 882871 |
| Contact Us | |
| Email: |
About Company
We address some of the world's most pressing global challenges and continue to develop new solutions. The population is constantly growing and its age is increasing. That is why it needs better medicines and high-quality food in sufficient quantities. Learn more about it here.