Caris Life Sciences is seeking a creative, driven, and goal-oriented principal data scientist to join their innovation team to develop machine learning algorithms using NGS, contribute towards liquid biopsy assay development, and design and implement analytic pipelines to support Caris' research initiatives.
Requirements
- Proficiency in programming in Python.
- Familiar with Linux environment and git.
- Experience with deep learning libraries such a PyTorch or Tensorflow.
- Knowledge of survival analysis and event data.
- Proficiency in the suite of AWS offerings (Sagemaker, HealthOmics, etc.)
- Knowledge of wet lab sequencing processes.
- Experience with interpretation of clinical health records including Electronic Health Records, insurance claims data, or patient histories.
Responsibilities
- Processing, manipulating, and analyzing large diverse data sets generated from NGS to develop biomarkers for cancer diagnosis, prognosis, and treatment.
- Developing, implementing, refining, and testing algorithms and workflows that achieve strategic goals in biomarker prediction, molecular profiling, and R&D objectives.
- Utilizing state of the art statistical, machine learning, deep learning, and survival analysis methods to analyze and interpret data and drive insights.
- Developing state-of-the-art predictive models with both structured and unstructured data sets.
- Responding to, and managing ad hoc requests to deliver accurate, in-depth analysis/results/data, in a timely matter.
Other
- PhD in Data Science, Computational Biology, Bioinformatics, Mathematics, Computer Science, Engineering, or a related field, with demonstrated exposure to cancer biology.
- At least 5 years of relevant experience in bioinformatics and data science.
- Strong analytical and critical thinking skills.
- Excellent communication abilities.
- Passion for personalized medicine and be committed to staying up to date with the latest developments in cell free DNA, machine learning, and deep learning technology.
