Principal Machine Learning Engineer, Graph Analytics - US

Mid / Senior

Fully Remote

Job Description

In this role, you’ll join our growing team of world-class engineers, statisticians, and clinical experts to deploy machine learning algorithms that help automate burdensome administrative clinical practices. You will be tasked with finding the most promising opportunities for impact and then delivering on them.


Our ML team’s work is focused in the following four areas:

  • Information retrieval from unstructured clinical text
  • Predicting clinical findings from structured and unstructured data sources
  • Building intelligent algorithms for ordering work queues
  • Identifying anomalies, such as behavior change and gaming, in user behavior


Responsibilities:

  • Perform in-depth analysis of healthcare data to independently design, develop, and deliver graph-based ML models and enhancements to our graph database.
  • Build reliable and scalable production machine learning systems.
  • Work on feature engineering, statistical analysis, developing novel ML techniques, understanding model performance, and ensuring fit-for-purpose.
  • Work cross-functionally across diverse stakeholders, including product managers, statisticians, EHR data specialists and physicians.
  • Optimize algorithms, running in real-time, for speed and accuracy.


Desired Profiles:

  • You have 5+ years' experience in applied ML in the industry with a degree or higher (MS/PhD) in computer science, machine learning, mathematics or similar field
  • Experience working with knowledge graphs, including: building and maintaining graphs, applying algorithms to graphs, and performing analytics on graphs.
  • Clear understanding of model building, model maintenance and the measures that optimize models for product use
  • Understand experimental design and can independently perform collection, measurement, and interpretation of results
  • Expert in Python or other common analytical data tools
  • Expert in Neptune or other common graph databases

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