Datavant is a data platform company and the world’s leader in health data exchange. Our vision is that every healthcare decision is powered by the right data, at the right time, in the right format. Our platform is powered by the largest, most diverse health data network in the U.S., enabling data to be secure, accessible and usable to inform better health decisions. Datavant is trusted by the world’s leading life sciences companies, government agencies, and those who deliver and pay for care. By joining Datavant today, you’re stepping onto a high-performing, values-driven team. Together, we’re rising to the challenge of tackling some of healthcare’s most complex problems with technology-forward solutions. Datavanters bring a diversity of professional, educational and life experiences to realize our bold vision for healthcare. Datavant is looking for an enthusiastic and meticulous Data Scientist (Privacy) to join our growing team, which interrogates and assesses client information in terms of the re-identification risk to patients. As part of the Privacy Science team within Privacy Hub you will play a crucial role in ensuring that privacy of patients is safeguarded in the modern world of data sharing. As well as working on real data, you will be involved in exciting research to keep us as industry leaders in this area, and stimulating discussions on re-identification risk. You will be supported in developing/consolidating data analysis and coding skills to become proficient in the analysis of large health-related datasets. You Will: Critically analyse large health datasets using standard and bespoke software libraries Discuss your findings and progress with internal and external stakeholders Produce high quality reports which summarise your findings Contribute to research activities as we explore novel and established sources of re-identification risk What You Will Bring to the Table: Excellent communication skills. Meticulous attention to detail in the production of comprehensive, well-presented reports A good understanding of statistical probability distributions, bias, error and power as well as sampling and resampling methods Seeks to understand real-world data in context rather than consider it in abstraction. Familiarity or proficiency with programmable data analysis software R or Python, and the desire to develop expertise in its language Application of scientific methods to practical problems through experimental design, exploratory data analysis and hypothesis testing to reach robust conclusions Strong time management skills and demonstrable experience of prioritising work to meet tight deadlines Initiative and ability to independently explore and research novel topics and concepts as they arise, to expand Privacy Hub’s knowledge base An appreciation of the need for effective methods in data privacy and security, and an awareness of the relevant legislation Familiarity with Amazon Web Services cloud-based storage and computing facilities Bonus: Experience of creating documents using LATEX Bonus: Detailed knowledge of one or more types of health information, e.g., genomics, disease, health images
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