Imaging Data Scientist, Arkana Laboratories

  • Bioinformatician
  • Postdoc
  • Scientist
  • Process and analyze digital images of tissues using commercially available platforms (eg. Halo, VisioPharm, SciLS, etc.); and (ii) to visualize and perform computational and statistical analysis of the resulting large datasets using languages such as R, Python, C++, Java and/or MATLAB.
Lab Website:

arkanalabs Little Rock, Arkansas


Arkana Laboratories is seeking an Imaging Data Scientist to work within the new Multiplex Imaging Center (MIC), part of our Transplant Translational Research Initiative in Little Rock, Arkansas. The Imaging Data Scientist is part of a dynamic team of researchers, scientists, and clinicians that engage with clinical trials operations, translational research initiatives, and research collaborators in academia, government and the pharmaceutical industry. The goals are to identify tissue biomarkers of immunologic mechanisms underlying clinical outcomes in human kidney transplantation, and to advance these imaging-based technologies toward clinical applications. The Imaging Data Scientist reports to the Director of the MIC and works shoulder to shoulder with the Multiplex Imaging Research Scientist in the MIC to process high dimensional digital images of tissue sections to quantitative data sets and performs downstream computational and spatial statistical analysis. In addition, the Imaging Data Scientist will have a background in data engineering that well-serves interaction between the MIC’s research enterprise and the company’s Information Technology department, advising and collaborating on best approaches toward innovation in data management.


• Digital image processing and analysis using commercially available software to derive quantitative image data sets.
• Computational analysis including data visualization and spatial statistical analysis of quantitative image data sets using R, Python, etc.
• Collaboration (internal and external) with researchers and pathologists throughout image processing and analysis stages.
• Support the development of new image analysis workflows and functionality needed for advancing the MIC’s contract services, collaborations with academic and industry, and projects in Arkana’s Transplant Translational Research Initiative.
• Serve as integration point for MIC with IT in aspects of data engineering to promote innovation and original thinking in execution of research tasks.
• Generate and present data for use in scientific technical content for internal use and public dissemination.
• Demonstrate proactive and original thinking in execution of tasks involved with all projects.
• Meet scheduled completion dates and provide timely feedback when applicable.

Qualifications/Preferred Skillsets:

• BS, MS, or PhD degree in bioinformatics, computer science, biology, biochemistry or similar; BS with 3+ years’ experience, MS with 1+ years’ experience, PhD with 0 years’ experience.
• Experience with developing approaches for computational analysis of large data sets.
• Experience in spatial statistics and machine learning.
• Experience presenting results to audiences with diverse scientific backgrounds.
• Excellent programming skills, for example, in R, Python, Java, C++, etc.
• Excellent written and verbal communication skills and the ability to clearly communicate scientific objectives and project results.
• Highly organized with attention to details and excellent information management, time management, and organization skills.
• Ability to learn quickly and work effectively, both independently and within a team environment.
• Self-motivated and energetic with a results-focused approach to achieving daily goals and activities.
• Experience in data engineering, including data warehousing; Unix, Linux, and open-source database software and tools; cloud services (EC2, AWS, etc); big data tools (Hadoop, Spark, etc) strongly preferred but not mandatory.
• Exposure or training in image processing software, quantitative image analysis, or geographical information systems software preferred but not mandatory.

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