PARTHENON THERAPEUTICS is a precision oncology biotech company dedicated to reprogramming the tumor microenvironment with the express intent of bringing recalcitrant cancers into submission. Our pipeline and drug discovery engine leverage an unprecedented approach for striking at immune-excluded tumors often found in recalcitrant cancers.

Parthenon Therapeutics is a leading company in tumor microenvironment research. We study the spatial interactions between tumor cells, immune cells, and extra cellular matrix, to understand the mechanisms of tumor escape from immune surveillance. You will join the Data Science team and work with senior staff to analyze large scale tumor multiple omics and phenome data, establish statistics and machine learning models, and perform data mining to facilitate novel therapeutics and biomarker discovery. You will also have the opportunity to work with scientists from different functional areas and contribute to our effort in drug discovery.

Role & Responsibilities:

  • Collecting and perform data engineering on public tumor multiple omics and phenome datasets
  • Design and implement statistic and machine learning models to establish data mining approaches
  • Model/pipeline optimization and large-scale analysis to identify genomic features
  • Implementing tumor genomic visualization

Job Qualifications:

  • The following skills are essential for this position:
    • Proficient in R, python, or other scientific programming languages
    • Strong background in statistics with good understanding of statistic test, regression, and data fitting models
    • Familiar with command lines and data manipulation in Linux
    • Familiar with widely used bioinformatics tools and databases
    • Self-starter with excellent problem-solving skills
    • Pursuing an academic degree in bioinformatics, biostatistics, data science, or a related scientific field
  • The following skills are helpful but not essential:
    • Good understanding in machine learning/deep learning models and experience in unsupervised learning for de novo classification
    • Familiar with cancer genomics datasets/tools, including TCGA, CBioPortal and DepMap
    • Experience in data analysis in High Performance Computer or AWS/GCP/AZURE
    • Previous experience in cancer biology

    Contact to apply.

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    Boston, MA 02135