Sayak Dhar
Associate Bioinformatics Engineer @Strand Life Sciences
Signup · Get unlimited contacts
WORK HISTORY
Associate Bioinformatics Engineer @Strand Life Sciences
Bengaluru, IN
Project 4]: Developing and pretraining a domain-adapted foundation model architecture for predictive modelling.[Project 3]: Fine-tuned LLMs for classification tasks on high-dimensional omics datasets and derived domain-specific insights.[Project 2]: Developed a multi-label deep learning framework for accurate prediction of biological outcomes associated with molecular compounds.[Project 1]: Implemented an end-to-end analysis pipeline for pathogen identification by integrating multi omics data.
EDUCATION
Calcutta University, Kolkata
Bachelor of Science - BSc, Microbiology Honours
Indian Institute of Technology, Bombay
M.Sc., Biotechnology
ABOUT SAYAK DHAR
I am a Data Scientist with a strong foundation in statistics, machine learning, deep learning and large language models, with experience in engineering, pre-training, and fine-tuning BERT- and GPT-based architectures for genomic sequence modeling and classification tasks. My work spans from literature-driven prototyping and exploratory data analysis to rigorous data cleaning and the design of advanced architectures using techniques such as curriculum learning, contrastive pretraining, and parameter-efficient tuning. I build scalable, distributed cloud pipelines that leverage spot instances for cost efficiency, deploy with MLOps best practices and incorporate real-time monitoring and logging to ensure high performance while optimizing speed and cost. I enjoy collaborating across disciplines, distilling complex concepts into clear insights and delivering presentations that support seamless coordination. With a strong interest in emerging methods - from CNNs and GNNs to foundation and state-space models, as well as multimodal integration, I strive to turn data into solutions that advance scientific understanding and create tangible business value.
This profile is compiled from publicly available professional sources. Unifers is not affiliated with or endorsed by LinkedIn. Request removal of this profile.