Associate Director, Data Science

Merck Merck · Pharma · Singapore

Associate Director for Imaging and Omics AI Analytics Research at Merck, focusing on developing and deploying AI/ML algorithms for multimodal medical images, omics data, and molecular biomarkers to discover novel biomarker solutions in healthcare. This role involves building foundational AI/ML capabilities and applying them to drug discovery and development.

What you'd actually do

  1. Spearhead the research and development of AI/ML algorithms to analyze multimodal medical images (e.g., MRI, CT, OCT), digital pathology and spatial-omics data, as well as molecular biomarkers and other omics datasets, with the goal of discovering and deploying novel biomarker solutions.
  2. Shape these efforts by collaborating with cross-functional teams to identify key research questions and develop robust AI/ML pipelines for integration into our applications, enhancing biomarker and data analytics solutions for various diseases, including cardiometabolic, immunological, and neurological conditions.
  3. Partner with internal teams of experts, external research institutions, and technology vendors to identify and access state-of-the-art AI/ML technologies and drive innovation to achieve significant outcomes.
  4. One key aspect of your role will be contributing to the development of reusable software/analytics pipelines that serve as foundational AI/ML capabilities, which can be integrated across various applications.
  5. Your efforts will contribute to transforming insights from medical and disease biology into biomarker solutions that enable decision-making in our drug development programs.

Skills

Required

  • PhD in Computer Science, Data Science, Engineering, Physics or related discipline (or Master's/Bachelor's with more experience)
  • 5+ years of experience applying AI/ML to biomedical research
  • Strong background in machine learning, deep learning, advanced neural network architectures (CNNs, RNNs, GNNs, Transformers)
  • Expertise in key learning paradigms (supervised/self-supervised/unsupervised learning, GANs, diffusion modeling)
  • Experience with foundation models/LLMs for large-scale medical imaging, omics, and text/EHR data analytics
  • Hands-on experience with modern programming languages (Python, R, Java, JavaScript)
  • Experience with ML/DL frameworks (scikit-learn, PyTorch, CUDA, TensorFlow)
  • Experience with cloud computing services (AWS, Azure, Google)
  • Experience with common ML engineering platforms/tools (MLFlow, Spark, Databricks, Dataiku)
  • Understanding of medical imaging modalities (MRI, CT, PET)
  • Leadership skills in building partnerships and leveraging relationships
  • Ability to engage and persuade others

Nice to have

  • Expertise in image processing techniques (image segmentation, registration, feature extraction)
  • Familiarity with tools like ITK, SimpleITK, OpenCV
  • Knowledge in cardiometabolic, immunological, and/or neurological disease biology
  • Strong interest in learning key concepts in disease biology and applying them to drug discovery

What the JD emphasized

  • COMPLEX
  • MULTIDISCIPLINARY
  • required to have a PhD
  • required to have a strong background
  • required to have understanding

Other signals

  • AI/ML algorithms for multimodal medical images, digital pathology, spatial-omics, molecular biomarkers, and omics datasets
  • Discovering and deploying novel biomarker solutions
  • Developing reusable software/analytics pipelines for foundational AI/ML capabilities
  • Harnessing big data (Biobank, real-world data, EHR) and data science for translation
  • Applying AI/ML technologies to biomedical research questions