Data Scientist
My retained client is a unique healthcare analytics and strategy consultancy organisation based in the Scottish borders. They are a commercialisation catalyst, fostering the drug innovation process and providing drug commercialisation assessment and strategic planning services to healthcare organisations around the world. A core element of the organisations work includes providing advice and validation relating to the Health Insurance Portability and Accountability Act of 1996 (HIPPA).
Culturally, the business has an extensive pool of expertise and is an excellent place to develop your career. The business offers a fantastic working environment combined with a flexible approach and supportive, knowledgeable team.
This role would equally suit a recent PhD or Masters graduate in a qualatative field, looking to take their first steps out of academia into the world of Data Science, or an individual with some experience in industry looking to take their next career step.
Overview of Role
The organisation works closely with healthcare data. In particular, advising clients working with Protected Health Information (PHI) on the risks associated with patient identification and compliance to HIPAA. Ensuring patient privacy is critical when working with PHI. A key element to ensuring that there is a very small risk of unauthorised identity or information disclosure, is through a range of statistical analyses on the underlying datasets(s) and analysis of data ingestion and processing methods. Due to an increasing workload, a Data Scientist is now required.
Requirements
As a Data Scientist, your main responsibilities will be to process and analyse large health-related datasets, and contribute to the subsequent preparation of associated reports.
You will have:
- Expertise in the manipulation, integration, processing and interrogation of large datasets.
- A practical understanding of how algorithms are designed, optimised and applied at scale.
- Expertise in at least one open-source data analysis software package (e.g., R, Python), with strong coding skills and annotation protocols.
- A good understanding of statistical probability distributions, bias, error and power as well as sampling and resampling methods.
- Developed skills in the application of scientific methods to practical problems through experimental design, exploratory data analysis and hypothesis testing to reach robust conclusions.
- Excellent report-writing skills. Well-developed time management skills and demonstrable experience of prioritising work to meet tight deadlines.
- Effective interpersonal and communication skills; the ability to work both part of a team and independently, without supervision as circumstances dictate.
- A strong intellectual curiosity with an interdisciplinary approach, drawing on innovation in academia and industry.
- An appreciation of the need for effective methods in data privacy and security, and an awareness of the relevant legislation.
