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Data Scientist I, Contracts & Out of Network

Oscar

Software & Data

Healthcare GHS Confidential
2 months ago

Job Summary

As a Data Scientist I, you will partner closely with our Market P&L, Tech and Legal teams to develop models and tooling to better understand Oscar’s medical costs across its networks. You will develop a deep understanding of healthcare reimbursement and use various data sources to inform negotiations, establish reimbursement for out-of-network providers, and help manage medical costs for Oscar members.

  • Minimum Qualification : Degree
  • Experience Level : Entry level
  • Experience Length : 1 year

Job Description/Requirements

Responsibilities

  • Responsible for the creation, implementation, and maintenance of data pipelines. This includes ensuring the accuracy, reliability, and timeliness of data flow with the support of a manager.

  • Support upkeep and enhancement of our models. This involves regular model testing, validation, and refinement to ensure the models remain effective and up-to-date with the latest industry standards and practices.

  • Develop advanced analytics and validation checks on data and statistical model outputs under the review of a manager at critical points. This includes identifying potential issues, anomalies, or inconsistencies in the data, and implementing corrective measures to ensure the integrity of our models and analyses.

  • Collaborate closely with data leaders and business teams to enhance overall data processes. This involves understanding their needs and requirements, providing them with the necessary data support, and working together to develop strategies and solutions that improve our risk adjustment processes and outcomes.

  • Compliance with all applicable laws and regulations

  • Other duties as assigned


Qualifications

  • A bachelor’s degree in a technical field OR 1+ years of relevant work experience

  • 1+ years experience working with SQL or Python

  • 1+ years experience building data models and using more advanced analytics methods, including conducting in depth analysis, rather than simple descriptive analysis  

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