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The Learning Center also works with a diverse portfolio of funders and partners - including Managed Care Plans, purchasers such as Covered California, and foundations including the California Healthcare Foundation and Blue Shield Foundation of California- to build Population Health Management (PHM)
The team develops and enhances various AI models, ML services and tools including LLM fine-tuning, alignment and optimization, RAG/Search, LLM evaluation and testing automation, feedback-based learning and guardrail for a wide range of applications in Airbnb. As a principal machine learning engine
You will design and ship predictive models and data products, improve the quality and interpretability of existing models, and deliver high-confidence, repeatable intelligence that fuels our MedTech-specific modules. The compensation offered for this role will be based on multiple factors such as lo
Strong quantitative abilities, distinctive problem-solving, and excellent analysis skills. Demonstrated experience using machine learning, deep learning, statistical methodology, and simulation/optimization modeling in geospatial, network topography, recommendation-systems, environmental systems and
Use SQL and Python to extract, join, and analyze data from relational databases and data warehouses. You should be strong in core data science and applied machine learning, comfortable working with real-world data, and capable of turning modeling work into production-ready systems. We’re hiring a
Balances team and individual responsibilities; Exhibits objectivity and openness to others' views; Gives and welcomes feedback; Contributes to building a positive team spirit; Puts success of team above own interests; Able to build morale and group commitments to goals and objectives; Supports every
Contribute analysis and perspective that inform portfolio-level decisions, including explaining model behavior, tradeoffs, and uncertainty to senior technical and business leaders. Develop deep understanding of borrower behavior, repayment dynamics, and portfolio structure across both products, and
Work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for machine learning models, drive engineering decisions, and quantify impac