Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time.
The ideal candidate has significant expertise in the biomedical or clinical domain, and is eager to apply his or her skills to improve patient outcomes.
What You Will Do:
- Design and prototype novel analysis tools and algorithms for electronic phenotyping on both structured and unstructured EHR data
- End-to-end large scale machine learning model integration.
- Collaborate with product, science, engineering, and business development teams to build the most advanced data solutions in precision medicine.
- Interrogate analytical results for robustness, validity, and out of sample stability.
- Document, summarize, and present your findings to a group of peers and stakeholders.
- PhD in a quantitative or computational field such as Computer Science, Computer Engineering, Machine Learning, Biomedical Informatics or related field.
- 3+ years of industry/academic experience in prototyping and building machine learning models for Electronic Health Records.
- Experience in modern Deep Learning and Natural Language Processing (NLP) techniques, including Transformers, seq2seq with attention, RNNs
- Experience with real world large scale machine learning.
- Experience with EHR data and data analysis: demographic variables, diagnostic codes, comorbidities, laboratory values
- 3+ years of programming experience in Python.
- Proficiency in frameworks like Pytorch, Tensorflow, or Keras.
- Experience building production-ready NLP systems.
- Experience designing and implementing large-scale distributed systems.
- Strong programming skills, familiarity with software development cycles, solid understanding of software concepts - data structures and algorithms.
- Thrive in a fast-paced environment and willing to shift priorities seamlessly.
- Experience with communicating insights and presenting concepts to diverse audiences.
- Team player mindset and ability to work in an interdisciplinary team.
- Goal orientation, self motivation, and drive to make a positive impact in healthcare.
- Ability to implement state-of-the-art algorithms in Document Classification, Text Analytics, and Transformers and Graph Neural Networks for EHR.
- Experience using biomedical knowledge information systems, such as UMLS, SNOMED CT, and RxNorm
- Good understanding of Statistics, probability calibration, operational cutoffs.
- Publications in SIGIR, CIKM, ACL, AAAI, KDD, EMNLP, ICML, ICLR, NeurIPS or equivalent.
- Clinical/Healthcare domain experience especially in Cardiovascular diseases is preferred.