Applied Machine Learning Engineer, Anomaly

• Standard knowledge of Python data-science stack
• 5+ years in software engineering experience
• Hacker mindset
• Experience building out production-ready machine learning solutions

Desirable

• Write production-level machine learning models
• Executing cutting-edge ML techniques to predict insurance payment amounts, denials, and overpayments with high precision, recall, and explainability
• Making infrastructural choices in order to ensure that the trained models scale well across hundreds of millions of health insurance claims
• Collaborate with the team to build internal tools to support our company mission
• Working with domain experts in the health insurance field to incorporate their knowledge into trained models
• Building interactive internal tools to obtain expert feedback for the purposes of model validation
• Have strong impact on innovative engineering projects
• Running unsupervised anomaly detection on millions of heterogeneous health-insurance features using PySpark
• Shining a bright light on black-box models to make all anomalous signals easily interpretable
• Interacting with domain experts and encoding their experience into simple heuristics. Pushing these heuristics to production for the purposes of weak supervision
• Training large-scale, weakly-supervised ML models using tools like Snorkel. Making sure these models are interpretable
• Tweaking models using expert feedback. Building dashboards to solicit that feedback using human-in-the-loop training techniques
• Experimenting with transformer model applications using the Pytorch / Tensorflow libraries