Data Science Co-Op

Data Science Co-Op 

 

 

 

 

 

In this co‑op 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, pre‑processing, 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 cross‑functionally;
• Good organization, documentation, prioritization, and scheduling skills, together with an overall desire to learn;
• Strong problem‑solving skills and critical thinking;
• Ability to learn new technical topics;
• Ability to work independently and multi‑task;
• Interest in machine learning applications (some experience is preferred);