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Job Description
Data Scientist (Full time) Report to: Data Science Manager in Pune
ARKRAY is a global healthcare company that is contributing to the health and well-being of people all over the world through the discovery of new technologies. With over 65 years in business, our products are now being used in 100 different countries worldwide and still growing; We are currently looking for the top talents who can help us accelerate our R&D team.
We’re building cutting-edge AI products in healthcare and are looking for a hands-on Data Scientist who thrives in fast-paced environments. You’ll work across computer vision, time-series, and miRNA/tabular data , owning problems end-to-end — from research to production.
If you enjoy turning messy real-world data into deployable AI systems and shipping quickly, this role is for you.
Own and build end-to-end ML/DL pipelines for:
Medical imaging (microscopy, urine sediment analysis)
Blood glucose prediction & continuous monitoring
Work across multiple data modalities :
Tabular biomedical data (e.g., Optimize models for real-world constraints (latency, compute, deployment environments).
Build deployable, scalable AI systems (not just notebooks).
Handle data end-to-end: preprocessing, validation, annotation, and database integration.
2+ years of experience in Data Science / ML / Deep Learning.
~ Strong hands-on experience in at least two of the following:
Computer Vision
Tabular ML (especially biomedical or high-dimensional data like miRNA)
~ Bachelor or above in either of Computer Science / IT, Data Science, or Major in Electrical/Electronics or Mechanical with minor in Data Science or Computer Science
Solid experience with Python + ML/DL frameworks (PyTorch/TensorFlow).
Experience building production-ready models (not just experiments).
PhD in either of Computer Science / IT, Data Science, or Major in Electrical/Electronics or Mechanical with minor in Data Science or Computer Science
Experience with Docker and cloud (AWS EC2, S3).
Familiarity with Generative AI / Agentic AI systems.
Experience optimizing models for edge devices or real-time systems.
Contributions to research papers, patents, or open-source projects.
Exposure to global collaboration and cutting-edge AI research
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