Principal Data Scientist - Innovation

Role Summary

Caris Life Sciences is seeking a creative, driven, and goal-oriented principal data scientist to join our innovation team. This position will be responsible for developing machine learning algorithms using NGS, contributing towards liquid biopsy assay development, as well as designing and implementing analytic pipelines to support Caris' research initiatives. The successful candidate will possess strong analytical and critical thinking skills, a solid scientific background, and excellent communication abilities. They should also have a 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. Additionally, there will be opportunities to publish research results and contribute to Caris' ongoing innovation efforts.

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.
• Collaborating with cross-functional teams, including bioinformaticians, molecular biologists, geneticists, and software engineers.
• 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.

Qualifications

• 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.
• Proficiency in programming in Python.
• Familiar with Linux environment and git.

Preferred Qualifications

• Experience with deep learning libraries such as 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.

Skills

• Machine learning, deep learning, survival analysis
• NGS data processing and biomarker development
• Data interpretation and communication of insights
• Collaboration with cross-functional teams

Education

• PhD in Data Science, Computational Biology, Bioinformatics, Mathematics, Computer Science, Engineering, or a related field

Additional Requirements

• Physical Demands: Working at a computer for the majority of the day.