Data Scientist

Datavant is the data collaboration platform trusted for healthcare. Guided by our mission to make the world’s health data secure, accessible and actionable, we provide critical data solutions for organizations across the healthcare ecosystem - including providers, health plans, researchers, and life sciences companies. From fulfilling a single patient’s request for their medical records to powering the AI revolution in healthcare, Datavanters are building the future of how data is connected and used to improve health. 

By joining Datavant today, you’re stepping onto a driven and highly collaborative team that is passionate about creating transformative change in 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 Solutions 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: 

• MSc or PhD in Statistics, Mathematics, Physics, Chemistry or similar.
• Proficiency with programmable data analysis software Python, and the desire to develop expertise in its language.
• 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.
• 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.
• Excellent communication skills. Meticulous attention to detail in the production of comprehensive, well-presented reports.
• Initiative and ability to independently explore and research novel topics and concepts as they arise, to expand Privacy Solution’ knowledge base.