Senior Deep Learning Scientist

Experience should be 15+ years and relevant experience should be 8+ on Deep learning

Role Overview :

As a Senior Deep Learning Scientist, you will play a pivotal role in developing and implementing advanced models for multimodality data to predict the toxicity of compounds, ultimately impacting patient outcomes. You will leverage cutting-edge deep learning techniques and work on a range of responsibilities to ensure the effectiveness and accuracy of the models.

Key Responsibilities:

· Perform in-depth exploratory data analysis for both image and tabular datasets.

· Conduct feature engineering to enhance model performance.

· Implement augmentation techniques to enhance the robustness of model.

· Handle batch correction for both image and tabular data to improve model performance.

· Handle multi-dimensional image data and develop segmentation pipelines to extract individual cells from images.

· Build deep learning models capable of predicting toxicity with confidence intervals.

· Collaborate with cross-functional teams to integrate deep learning models into existing workflows.

· Develop and maintain documentation for model processes and findings.

· Present research findings and updates to stakeholders.

· Optimize computational resources and improve model efficiency.

· Deploy models in platform of interest.

Preferred Experience

· Prior experience with Contrastive learning approaches such as DINO, SimCLR, BYOL for images.

· Familiarity with transformer-based architectures for tabular data, such as TabTransformer.

· Proficiency in PyTorch Lightning and PyTorch for model development and training.

· Hands-on experience in designing, training, and deploying advanced deep learning models.

· Theoretical understanding of deep computer vision models, including image classification, segmentation, multi-modality models, diffusion models, and large language models.

· PhD in machine learning, computer science, math, or physics.

· Established track record of publications in top-tier conferences or journals.

· Hands-on experience in training, debugging, and deploying deep learning models.

· Proficient coding skills in machine learning products, not limited to research prototypes.

· Motivated to stay updated with the latest research in deep learning and contribute to product improvement.

· Strong problem-solving skills and the ability to work independently.

· Excellent communication skills to effectively convey technical concepts to non-technical stakeholders.