Caris Life Sciences is looking to expand its suite of histopathology biomarkers by developing computer vision-based machine learning algorithms and analytic pipelines to drive research initiatives forward.
Requirements
- Proficiency in at least one general-purpose programming language (e.g., Python, Java, C++), with a preference for Python.
- Experience in Linux environments and version control systems like Git.
- Ability to query and manipulate both relational (SQL) and non-relational (NoSQL) databases.
- Practical experience with machine learning/deep learning frameworks such as PyTorch, TensorFlow, Keras, or OpenCV.
- Hands-on experience with classification, segmentation, and object detection tasks.
- Familiarity with end-to-end data science workflows, including model development, validation, and deployment.
- Experience working with medical imaging data, especially histopathology images from whole slide scanners.
Responsibilities
- Design, implement, refine, and test algorithms and workflows to support strategic goals in image biomarker discovery, molecular profiling, and translational R&D.
- Collaborate with pathologists and scientists to develop ground truth datasets and diverse feature sets from whole slide images (WSIs) and other modalities.
- Build predictive models using both structured and unstructured data, integrating imaging, molecular, and clinical data.
- Process and analyze large, multi-source datasets across various data types.
- Support ad hoc analysis and reporting requests with timely, accurate, and interpretable results.
- Follow best practices in code development, documentation, and model delivery within a collaborative team setting.
- Review and provide guidance on modeling approaches, experimental design, and code quality across the team.
Other
- PhD in Data Science, Computational Biology, Computer Science, Engineering, Mathematics, or a related field with demonstrated exposure to cancer biology.
- At least 2 years post-PhD work experience.
- Strong understanding of biological and pathology-driven questions, with the ability to develop statistical and machine learning solutions.
- Strong written and verbal communication skills.
- Demonstrated ability to lead projects, mentor junior scientists, and collaborate across multidisciplinary teams.
