at Merck in Springfield, Illinois, United States
Job Description
Job Description
Senior Scientist , Quantitative Pharmacology and Pharmacometrics, AI/ML
The Quantitative Pharmacology and Pharmacometrics (QP2) department drives model-informed drug discovery and development (MIDD) to routinely enable efficient drug discovery/development and/or regulatory decisions. The Pharmacometrics group within QP2 brings an experienced leadership team, deep modeling expertise and state-of-the-art modeling approaches across multiple therapeutic areas and modalities to drive portfolio impact from discovery through life-cycle management. The team is externally visible and continues to be at the leading edge of building innovative state of the art tools. With MIDD as an essential component of decision-making at our company this position offers an opportunity for the Senior Specialist to drive pipeline impact across therapy areas.
The Senior Specialist will develop and execute data science strategies leveraging artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) techniques to enable model-informed drug development by partnering with scientists across QP2 to frame the critical drug development questions that these techniques can address and develop appropriate AI/ML-based strategies and solutions. The Senior Scientist will be part of a team responsible for integrating the AI/ML techniques within QP2 and will maintain knowledge of the rapidly evolving AI/ML landscape in search of the best strategies to enable QP2’s modeling analyses.
The ideal candidate is collaborative, eager to learn and not intimidated by tough scientific challenges. Excellent communication skills are required, including the ability to clearly present technical and strategic concepts in group settings. Experience applying machine learning methods and flexibility adapting these methods to unique use-cases in pharmacology is required.
QP2 offers a unique opportunity to accelerate your professional career as a quantitative drug developer taking advantage of our end-to-end pipeline support in one of the world’s most research-intensive biopharmaceutical companies.
Required Experience:
+ A Ph.D. or equivalent degree – OR- a MS or equivalent degree with at least four years of experience, where “experience” means having a record of increasing responsibility and independence in a similar role in pharmaceutical drug development or academia
+ An educational background in data science, machine learning, computer science, statistics/ biostatistics, mathematics, computational biology/chemistry, chemical/biomedical engineering, pharmaceutical sciences, or pharmacometrics.
+ Proficiency in application of machine learning techniques for classification/regression, feature selection, and/or reinforcement learning.
+ Understanding of generative and non-generative NLP approaches for automated information extraction
+ Understanding of current AI/ML and NLP methods and best practices.
+ Proficiency in R, Python, Julia, C/C++, or other similar programming language and machine learning frameworks such as scikit-learn, Keras, PyTorch, or similar
+ Proficiency in written and verbal communication
Preferred Experience:
+ Experience in applying machine learning techniques to biological or biomedical problems
+ Experience with High Performance Computing environments, Linux, and Bash scripting
+ Experience with Amazon Web Services and Jupyter Notebooks
+ Experience with pharmacometric analyses (e.g. population PK/PD, translational PK/PD, QSP modeling) and software (e.g. NONMEM, Monolix, Phoenix, Matlab, etc.)
+ Skills in experimental design, mathematical problem solving, critical data analysis/interpretation, and statistics
Your role at our company is integral to helping the world meet new breakthroughs that affect generations to come, and we’re counting on your skills and inventiveness to help make meaningful contributions to global medical advancement. At our company, we’re inventing for life.
\#EBRG
“This (remote) position is only applicable for those that are not within commutable distance to primary sites noted in posting. Please note that standard commute is (less than) <50 miles.”
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