Full-Time

Associate Director

Machine Learning

Confirmed live in the last 24 hours

MSD

MSD

Compensation Overview

$167.7k - $263.9kAnnually

+ Bonus + Long Term Incentive

Senior, Expert

H1B Sponsorship Available

North Wales, PA, USA + 1 more

More locations: San Bruno, CA, USA

Position available in South San Francisco, CA or West Point, PA based on candidate preference.

Category
Applied Machine Learning
Deep Learning
AI & Machine Learning
Required Skills
Python
Data Science
Tensorflow
R
Git
Pytorch
AWS
Pandas
NumPy
Linux/Unix
Databricks
Requirements
  • Ph.D. (with 4+ years) or Master’s (with 8+ years) in computer science, computational biology, cheminformatics or related data science fields with relevant years of experience
  • Proven experience in applying traditional machine learning methods as well as, Deep Neural Networks, Generative AI and LLM on data from pharmaceutical or other industry sectors
  • Fluency in python, R programming, standard python packages like Pandas, NumPy, Matplotlib, and ML frameworks such as TensorFlow, Pytorch
  • Experience with version control and related code reproducibility practices such as git documentation
  • Experience with Linux and high-performance or cloud computing environments (e.g., AWS) and data lake platforms (e.g., Databricks)
  • Self-motivated with a high level of autonomy
  • Exceptional communication, presentation, and collaboration skills to effectively distill complex technical concepts for a broad range of stakeholders is necessary
  • High interpersonal skills with a collaborative mindset
  • Domestic travel 10-20% is required
  • Business Intelligence (BI), Database Design, Data Engineering, Data Modeling, Data Science, Data Visualization, Machine Learning, Software Development, Stakeholder Relationship Management, Waterfall Project Management
Responsibilities
  • Individual contributor role, requiring mentorship of junior scientists
  • Prepare ML model-ready dataset by curation, integration, and harmonization of multimodality data from the internal and external domains, in partnership with the subject-matter experts within NDS and our company's Research IT teams
  • Apply probabilistic, neural networks ML models, and generative AI methods to inform prioritization for chemistry and toxicology resources
  • Leverage LLM and generative AI models (e.g. GAN, VAE) to understand mechanisms of toxicity, identify molecules with desired drug-like properties, to prioritize animal resources
  • Work collaboratively with colleagues across multiple sites and functional areas to deploy, utilize, and increase the visibility of ML approaches in selection of chemical series with a high probability of success, and enable prioritization of *in vivo* resources
  • Upscale NDS staff on the utility of predictive AI/ML approaches in drug safety
  • Stay abreast with new AI approaches and regulatory landscape in the field of predictive toxicology

Company Stage

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Total Funding

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Headquarters

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Founded

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