Senior AI/ML Data Scientist

$122,800 - $193,300/Yr

Merck & Co. - Boston, MA

Full-time - Mid Level

Onsite - Boston, MA

Chemical Manufacturing

About the position

The Computational Toxicology group within the Nonclinical Drug Safety (NDS) division at our company is actively seeking an AI/ML Data Scientist to contribute to the discovery and development of effective, safer therapeutics for patients. This key role will be instrumental in supporting NDS's broader mission to be at the forefront of AI/ML application and data science methodologies towards drug safety prediction. The ideal candidate will possess a strong foundation in Artificial Intelligence (AI)/Machine Learning (ML) and will be integral to the development of predictive ML models for safety and toxicology endpoints, using a variety of data modalities from drug discovery, preclinical development, clinical trials, and post-market surveillance. The candidate will implement AI methodologies to enable early safety hazard identification, generate viable hypotheses for mechanistic toxicology, and ensure the early adoption of these models into our company's discovery and development pipeline. In this role, the successful candidate will inform prioritization for in vitro and in vivo preclinical toxicology resources by applying probabilistic, neural networks ML models, and generative AI methods. They will utilize LLM and Generative AI approaches to generate hypotheses for mechanistic toxicology. Collaboration is key, as the candidate will work with colleagues across multiple sites and functional areas to deploy, utilize, and increase the visibility of ML approaches in the selection of chemical series with a high probability of success, and/or enable prioritization of in vivo resources. Additionally, the candidate will be responsible for upscaling NDS staff on the utility of predictive AI/ML approaches in drug safety and staying abreast of new AI approaches and the regulatory landscape in the field of predictive toxicology.

Responsibilities

• Inform prioritization for in vitro and in vivo preclinical toxicology resources applying probabilistic, neural networks ML models, and generative AI methods.
• Utilize LLM and Generative AI approaches to generate hypotheses for mechanistic toxicology.
• 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.
• 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.

Requirements

• Master's (with 4+ years) or Ph.D. in computer science, computational biology, cheminformatics, biomedical engineering, and related data science fields and relevant experience.
• Fluency in Python, R programming, standard Python packages like pandas, numpy, matplotlib, and ML frameworks such as TensorFlow or PyTorch.
• Experience with various statistical ML techniques, including both supervised and unsupervised learning as well as DNN architecture is required.
• First-hand experience using Generative AI and LLM methods in related data science field would be advantageous.
• Experience with version control and related code reproducibility practices such as git, documentation.
• Self-motivated with a high level of autonomy.
• High interpersonal skills with a collaborative mindset.

Nice-to-haves

• Prior experience with high-performance or cloud computing environments (e.g., AWS) and data lake platforms (e.g., Databricks).
• Experience with machine learning (ML) models deployment frameworks such as MLOps.

Benefits

• Bonus eligibility
• Long term incentive if applicable
• Health care and other insurance benefits (for employee and family)
• Retirement benefits
• Paid holidays
• Vacation
• Sick days