Precision Health AI (PH.AI) is developing the leading artificial intelligence (AI) platform for oncology built on deep clinical oncology data to enable the practice of precision medicine for better cancer patient care and to accelerate cancer-related drug development, trials, and real world evidence for the benefit of patients, oncologists, and the cancer care ecosystem. To learn more, visit www.precisionhealth.ai
As a healthcare technology company, PH.AI’s AI platform is trained specifically for oncology, leverages proprietary and unique data sources, and integrates seamlessly into the existing workflows of healthcare manufacturers, providers, and payers, with the goal of improving oncology outcomes. With the aid of its industry-leading AI platform, PH.AI analyzes large sets of cancer patient data from a variety of settings including observational studies, clinical trials, and real world evidence such as genomic sequencing data, EMR/EHR, and claims, to identify which treatments work for which cancer patients, why, in what context, and at what cost, with the goal of developing tailored treatment regimens for each patient. PHAI is unlocking the promise of precision medicine, one cancer patient at a time.
As a data scientist, you will be responsible for analyzing and modeling problems in the clinical oncology space using on our real world clinical data asset. Your work will serve client driven initiatives as well product and platform R&D. As such, you will report to the head of data science and head of client services. You should have a good mix of programming/CS, visualization, stats/math, and ML/AI, and ideally, you have been applying those skills in healthcare. You will work with subject matter experts to design models and quantitative methods around client driven needs, and you will work with engineers to write software and productize your work.
- Understand client and product needs and translate them into tactical R&D initiatives.
- Collaborate on software projects with engineers, providing analytical guidance and following best practices.
- Reason about healthcare specific problems to analyze, model, interpret, and deliver results.
- Run a variety of analytics from simple queries, to data QA, and complex models and algorithms
- Understand the differences and tradeoffs between various ML/Stats techniques and be able to select them appropriately, and apply them in a meaningful way
- Implement models using high level software packages (SKlearn, R, TensorFlow, Spark)