Passionate about precision medicine and advancing the healthcare industry?
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 is passionate and committed to use the best NLP techniques to sanitize, disambiguate, extract and assemble vital clinical and genomic data for purpose of finding cure to cancer, cardiovascular and other diseases.
What You Will Do:
Work with a stellar team of data scientists and engineers working with large corpuses of health records from variety of sources.
Work with lead to experiment various NLP modeling techniques, fine tune and improve model.
Paying attention to details, identify variation and ambiguous cases in test and production environment.
Participate in design in text processing pipeline and model design, clearly documenting methodology, architecture workflow and results.
Carry an open mind, willingness to learn and dive deep, grow to take ownership of generalized model in production.Required Qualifications:
PhD, Masters with 2+ years or Bachelor with 3+ years experience in a quantitative or computational field such as Computer Science, Machine Learning, Statistics, Biomedical Informatics or related field.
Experience in modern Deep Learning and Natural Language Processing (NLP) techniques and frameworks, including Transformers, seq2seq with attention, RNNs, gensim, fasttext, spacy, scikitlearn, pandas
Proficient with python, pandas, numpy, nltk, gensim for EDA and text analytics.
Experience in frameworks like Pytorch, Tensorflow, or Keras.
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.
Understand how to leverage word embeddings, skill with word sense disambiguation.
Experience taking to production models in Text Classification, Named Entity Recognition, relation extraction, unsupervised and supervised ML methods, understand trade offs between classic and SOTA Transformer models.
Understand BERT architecture and its variants, experience with huggingface framework
Working knowledge of AWS, GCP, Spark, data warehousing.
Experience using biomedical knowledge information systems, such as UMLS, SNOMED CT, and RxNorm
Publications in SIGIR, CIKM, ACL, AAAI, KDD, EMNLP, ICML, ICLR, NeurIPS or equivalent.
Clinical/Healthcare domain experience especially in Cardiovascular diseases is preferred.