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AbbVie hat es sich zur Aufgabe gemacht, innovative Arzneimittel zu entdecken und bereitzustellen, die schwerwiegenden Gesundheitsprobleme von heute bekämpfen und den gesundheitlichen Herausforderungen von morgen gewachsen sind. Unser Ziel ist es, das Leben der Menschen in verschiedenen wichtigen Therapiebereichen wie der Immunologie, der Onkologie, den Neurowissenschaften, der Augenheilkunde, der Virologie, der Frauengesundheit und der Gastroenterologie zu verbessern. Mit dem Portfolio von Allergan Aesthetics bieten wir darüber hinaus Produkte und Dienstleistungen im Bereich medizinische Ästhetik an. Für weitere Informationen über AbbVie besuchen Sie uns unter www.abbvie.com. Folgen Sie @abbvie auf Twitter, Facebook, Instagram, YouTube und LinkedIn.

Science & Technology Visiting Scientist - Data Science

North Chicago, Illinois Anzeigen-ID 2205413 Kategorie Manufacturing & Operations Bereich AbbVie

The Operations Science and Technology program is intended to be a yearlong assignment that seeks to provide opportunities for Ph.D level scientists and engineers with tangible work experience in the biotech pharmaceutical industry.  During the year, associates in the program will work on meaningful and highly valued projects in a designated area of the function.  Mentoring opportunities will be available as well as access to AbbVie’s leadership training and development curriculum.  Associates will  gain invaluable, on-the-job experience in one of the functions of the Operations S&T organizations in North Chicago, IL and Worcester, MA. Functions, which support multiple aspects of drug development and commercialization.

Project Scope:  Integrating Data Science with Machine Learning (DS-ML) to Develop Predictive Modeling and Advance System Reliability for Pharmaceutical Product Applications

Responsibilities:  

  • Provide the scientific and engineering function support with data science (DS) and machine learning (ML) for reliability engineering for Combination Products and Medical Devices products.
  • Collaborate with stakeholders to define a problem, build a dataset, compare various machine learning algorithms, output results, and interpret and present those results.
  • Make value out of data, as the scientist/student proactively draws information from various sources and analyzes it for better understanding of how product reliability performs, and builds artificial intelligence (AI) tools that automate certain processes for science and engineering applications.
  • Create various machine learning-based tools or processes for the product reliability applications, recommend preferred direction and process for automated lead scoring systems. Should also be able to perform statistical analysis.

We Offer You:  

  • The opportunity to learn about pharmaceutical product development and commercialization. How to understand and use data from different sources, including development data, manufacturing data, clinical data, human factors data, and other information, allowing data science and machine learning (DS-ML) to analyze, extract trends, and explore their interactions for product improvements and risk mitigation. 

#LI-DNI

#LI-DNI


  • Recent (within 1 year) graduate of a doctoral program (PhD) or current doctoral students in their last year.
  • Proficiency in using query languages such as SQL and Hive, experience with NoSQL databases, such as MongoDB, Cassandra, and/or HBase, good at applied statistics skills, such as distributions, statistical testing, regression, and others.
  • Experience with common data science toolkits, such as R, Weka, NumPy, MatLab, and others.
  • Experience working in research labs or industrial biotech through internships.
  • Possesses a strong academic performance and publication record.
  • Authorized to work in the U.S.
  • Strong communications skills and a self-starter.
  • Ability to work effectively in a team environment.

Significant Work Activities: N/A
Travel: No
Job Type: Recent Graduate
Schedule: Full-time
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