What are the responsibilities and job description for the Image Data Scientist position at Akoya Biosciences?
As The Spatial Biology Company®, Akoya Biosciences’ mission is to bring context to the world of biology and human health through the power of spatial phenotyping. The company offers comprehensive single-cell imaging solutions that allow researchers to phenotype cells with spatial context and visualize how they organize and interact to influence disease progression and response to therapy. Akoya offers a full continuum of spatial phenotyping solutions to serve the diverse needs of researchers across discovery, translational and clinical research via its key platforms: PhenoCycler™, PhenoImager™ Fusion and PhenoImager HT.
Position Summary:
The Image Data Scientist is an individual contributor and a key role to provide biological insights via quantitative, statistical, and spatial interrogation of the collected image data in support of discovery/RUO, LDT, and regulated product development activities for diagnostic tests developed internally and via partnerships. The ideal candidates will be able to design and execute analytical exercises by applying deep understanding of various data analysis techniques relevant to image data, with the ability to select the optimal approach for an experiment.
The candidate will work in a supportive culture within a cross functional team, creating industry-leading medical technology products. This position will also have growth opportunities that support both personal and professional development.
Duties & Responsibilities:
- Collect, organize, and analyze various microscopic image data (brightfield and fluorescence)
- Evaluate and apply various machine learning and computer vision algorithms for prototype algorithm development including feature extraction, feature selection, and clustering/classification
- Visualize complex data in interpretable way to provide feedback on the subject of experimental design for quantification of image data, by leveraging machine learning and statistics tools
- Effectively support collaborative research projects
- Designs and develops innovative algorithms to analyze histopathology images and data
- Partners with research, development, and product development teams within Akoya; networks with internal and external stakeholders (e.g., pathologists, biochemists, instrumentation engineers, software engineers, and marketing representatives) and subject matter experts in Akoya and in academia.
- Support strategic decisions to optimize algorithms and platforms for efficiency, accuracy, performance, and scalability.
- Work closely with key members of the cross-functional team- evaluating and developing customized data analytics tools, providing relevant data with key stakeholders (e.g. Assay development, Biostatistics, and Advanced Biopharma Service)
- Works effectively both as an individual and part of a team; can manage cross functional relationships effectively.
Skills & Requirements:
- D. or MS 2 years relevant experience in data science, statistics, machine learning, image analysis, digital pathology and/or related field.
- Competency in MATLAB, Python, and R programming
- Experience in applying statistical modelling, machine learning, exploratory, and confirmatory data analysis in mid to large volume data sets.
- Experience in microscopic image processing and analysis.
- Works effectively with both internal and external audiences to analyze, interpret, and summarize data orally and in writing; ability to draft technical reports for communication of progress and results. Ability to effectively communicate plans to the immediate team and coordinate resources for execution of assignments.
- Work across the stage of any experiment from technical feasibility to analytical validation of a commercial product
- Able to work with significant independence
Travel:
- Potential for travel is 10-15% to work with key partners and internal colleagues.
Akoya Biosciences, Inc. proudly affords equal employment opportunity to all qualified persons regardless of race, color, religious creed, national origin, age, military status, sexual orientation, disability, genetic information, gender identity, gender expression or gender unless based upon a bona fide occupational qualification.
