Associate Director, Computational Biology /Toxicology

Job Description

The Nonclinical Drug Safety (NDS) division at our company is seeking a motivated Computational Biologist/Toxicologist for our Computational Toxicology group. The successful candidate will have a passion for using computational biology and advanced data science approaches, including artificial intelligence (AI) and machine learning (ML), to inform drug development and safety.

Key Responsibilities:

• Individual contributor role, requiring mentorship of junior scientists

• Leverage expertise in computational biology, bioinformatics, and machine learning to create a pipeline for the analysis of multimodal data including but not limited to multi-omics, chemistry, drug metabolism and pharmacokinetics, and in vitro/in vivo toxicology data, aiding in the comprehension of toxicity mechanisms

• Lead the development of AI/ML models to construct advanced models for organ-specific toxicity and safety phenotypes across multiple modalities

• Work both independently and collaboratively with toxicologists, compound leaders, and assay owners within NDS, as well as researchers from other functional areas outside NDS, to enhance drug safety evaluations within and across multiple projects

• Influence the regulatory toxicology landscape through active participation in external consortiums, publications, and presentations at key scientific conferences

Qualifications:

Education:

• Ph.D. (with 4+ years) or Master’s (with 8+ years) in computational biology, computational chemistry, bioinformatics, computer science or related data science fields with relevant years of life sciences industry experience

Required Experience and Skills:

• Proven experience in analyzing various data types including transcriptomics, proteomics, metabolomics, molecular, and imaging data

• Demonstrable experience applying advanced AI/ML techniques, including but not limited to Deep Neural Network Frameworks, in problem-solving scenarios

• Proficiency in Python, R programming, and ML frameworks such as TensorFlow and PyTorch is essential

• Experience with version control systems, such as GIT, and related code reproducibility practices, such as documentation, is required

• Exceptional communication, presentation, and interpersonal skills to effectively distill complex technical concepts for a broad range of stakeholders is necessary

• A highly self-motivated individual with the ability to work independently

Preferred Experience and Skills:

• Proficiency in creating effective visualizations using platforms such as seaborn, matplotlib, ggplot2

• Experience with high-performance or cloud computing environments (e.g., AWS) and data lake platforms (e.g., Databricks)

• Prior experience working in a multidisciplinary environment within the Pharmaceutical Industry, Regulatory Agencies, or Academic Institutions

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