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We are seeking a dedicated Data Scientist to join our team working on the Hospital Quality Reporting system for the Centers for Medicare and Medicaid Services (CMS). The ideal candidate will be an analytical thinker with a deep understanding of healthcare data and its intricacies. Your work will play a pivotal role in improving the quality and effectiveness of healthcare services provided to millions of beneficiaries.
- Analyze and interpret complex healthcare datasets to provide insights into hospital performance, patient outcomes, and care quality.
- Develop, test, and deploy predictive models to identify areas of improvement and risk within hospital care.
- Collaborate with cross-functional teams to integrate your findings and models into the Hospital Quality Reporting system.
- Design and implement data visualization tools to communicate findings and recommendations to non-technical stakeholders.
- Work closely with CMS to understand their requirements, ensuring data analysis aligns with their goals and objectives.
- Work closely with Academic Medical Professionals (e.g. UCDavis & AIR) to translate the existing inpatient rules for claims to develop models on mandated updates.
- Stay updated on current healthcare data standards, ensuring all analysis is compliant with relevant regulations.
- Provide ongoing support for the Hospital Quality Reporting system, ensuring its reliability and accuracy.
- Bachelor's degree in Data Science, Statistics, Computer Science, or a related field.
- 5+ years of experience in data analysis or data science, preferably in a healthcare setting.
- Proficiency with data analysis tools such as R, Python, SQL, and others.
- Strong knowledge of machine learning algorithms and statistical methods.
- Experience with big data platforms and tools like SAS, AWS Glue, Databricks, Spark, or similar.
- Ability to communicate complex data findings in a clear and concise manner to both technical and non-technical audiences.
- A deep understanding of the healthcare domain, especially within the context of CMS.
- Familiarity with Hospital Quality Reporting measures and CMS regulations.
- Experience working with Electronic Health Records (EHR) data.
- Knowledge of health information standards such as HL7 or FHIR.
- Prior experience collaborating with government agencies or healthcare policymakers.
- Demonstrated ability to manage and execute large-scale projects with minimal oversight.
- Proficiency in data visualization tools like Amazon QuickSight, Tableau, PowerBI,Viya/R Studio, or similar.
- Three weeks paid leave
- Flexible Schedule and Remote-First culture
- $3000 annual education stipend
- Work from home setup including Macbook (possibly used/refurbished)
- Collaborative, learning environment
- Health insurance
- 401K Plan with Safe Harbor contribution
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Executive Order requiring COVID safety protocols
Bellese employees are Federal Workers and are subject to Executive Order 14042, Ensuring Adequate COVID Safety Protocols for Federal Contractors, dated September 9, 2021 (published in the Federal Register on September 14, 2021, 86 FR 50985). As condition of employment, employees will need to provide either: a: proof of completed COVID vaccinations OR b: have an approved medical or religious exemption and proof of weekly COVID testing.
U.S. citizen or legal right to work in the United States without sponsorship
Bellese Technologies, LLC is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability. In addition to federal law requirements, Bellese Technologies, LLC complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.
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