Data Scientist (USA)

 Posted 2 days ago
     
5-10 years experience
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AI Summary

Develop and evaluate ML, deep learning, and LLM-based workflows for digital pathology and oncology data. Collaborate with interdisciplinary teams to translate biomedical research questions into functional AI solutions.

We’re looking for a Data Scientist to join our growing team and help develop AI solutions at the intersection of oncology, precision medicine, and drug development.

In this role, you will work closely with data scientists, engineers, scientists, and product to turn complex biomedical data into meaningful AI-driven workflows. You will contribute to research, experimentation, and model development across digital pathology, clinical data, and LLM-based applications.

This is a great opportunity for someone who wants to grow in a highly interdisciplinary environment, learn from an experienced team, and take part in building technology that can support the future of precision oncology.


Responsibilities

  • Develop and evaluate ML, deep learning, and LLM-based workflows for digital pathology and oncology data.
  • Build data and model pipelines for biopsy images, clinical records, reports, and other biomedical datasets.
  • Use LLM tools and coding assistants to accelerate research, analysis, prototyping, and workflow automation.
  • Design and run experiments, analyze model performance, and translate results into clear conclusions.
  • Collaborate with data scientists, engineers, pathologists, and clinical experts to turn research questions into working AI solutions.

Requirements

  • BSc or MSc in Software Engineering, Computer Science, or a related field
  • 5+ years of experience in deep learning and machine learning algorithm development.
  • 3+ years of experience developing instance segmentation models.
  • 2+ years of experience working with LLMs and prompt engineering.
  • Experience using AI coding tools such as Cursor, Claude Code, or similar tools.
  • Strong analytical skills, attention to detail, and ability to learn quickly.

Position terms:

  • Location: Remote (in the USA)

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