Associate Director, Data Science - Cambridge, United States - Merck

Description

Job Description

Our Artificial Intelligence Machine Learning (AI/ML) capabilities are critical accelerators to our mission to delivering towards inventing new medicines that save and improve lives. Core to the Data, AI, and Genome Sciences (DAGS) function is an AI/ML-first approach to improving target and biomarker discovery, validation and selection and elucidating complex disease mechanisms. As Associate Principal Data Scientist for Graphical Neural Networks you will be responsible for developing and implementing advanced methodologies and techniques to identify disease biology from multi-modal data using graph-based approaches. Your expertise in knowledge graphs and graphical neural networks will contribute to the advancement of our understanding of complex diseases and support the development of innovative therapeutic strategies. You will be part of a cross-functional team of computational biologists, bioinformaticians, data scientists, software engineers, and machine learning engineers that strive to identify therapeutic targets.

Primary Responsibilities:

• Collaborate with cross-functional team to identify research questions, data requirements, and develop appropriate machine learning solutions.
• Apply graph-based techniques to model and analyze complex relationships between biological entities, uncovering disease mechanisms and identifying potential therapeutic targets.
• Develop deep learning architectures and algorithms tailored for graph-based data, combining graph convolutional networks (GCNs), cutting-edge methodologies for graph-based representation learning, graph attention networks, and transfer learning to enhance disease biology understanding.
• Stay up to date with the latest advancements in Graphical Neural Networks, Knowledge Graphs, and related fields, and apply relevant advancements to improve existing methodologies and models.
• Publish research findings in relevant conferences and journals, and actively contribute to the scientific community through knowledge sharing and collaborations.

Required Experience and Skills:

• PhD in Computer Science, Applied Math, Physics, or a related field and 3+ years academic medical and/or industry experience.
• Strong expertise and experience in machine learning, Graphical Neural Networks (GNNs), knowledge graphs, and graph-based technology and analysis.
• Experience and solid understanding of graph representation learning, graph convolutional networks (GCNs), and related methodologies.
• Proficiency in programming languages such as Python, and experience with deep learning frameworks and libraries like PyTorch, PyG, etc.
• Interest in life sciences problems and disease biology, and willing to learn from and teach others.
• Excellent communication skills and ability to work collaboratively in multi-disciplinary team.

Preferred Skills and Experience:

• Familiarity with life science data is a plus.
• Relevant publications in scientific journals and experience contributing to research communities, including NeurIPS, ICML, ICLR, etc

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Job Posting End Date:05/28/2024

A job posting is effective until 11:59:59PM on the day BEFORE the listed job posting end date. Please ensure you apply to a job posting no later than the day BEFORE the job posting end date.

Requisition ID:R294554