At a Glance
- Tasks: Analyse clinical trial data and creatively present findings in various formats.
- Company: Costello Medical, a leading firm in healthcare analysis.
- Benefits: Hybrid working options, competitive salary, and ongoing training.
- Other info: Opportunities for career growth and collaboration with clients.
- Why this job: Join a dynamic team and make a real difference in healthcare research.
- Qualifications: Strong analytical skills and a passion for healthcare.
The predicted salary is between 29700 - 36300 £ per year.
Costello Medical is seeking an Analyst to work on detailed analysis of clinical trial data and the creative presentation of this analysis in posters, slide sets and peer-reviewed publications.
The role is full-time and permanent, with hybrid working options across the UK offices in Cambridge, Manchester, Bristol and Edinburgh.
You will join project teams, receive one-to-one training, and collaborate with clients while delivering multiple projects across disease areas.
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Hybrid Analyst: Value & Evidence for Clinical Trials employer: Costello Medical, Inc.
Costello Medical is an exceptional employer that fosters a collaborative and innovative work culture, providing employees with the opportunity to engage in meaningful projects across various disease areas. With a strong emphasis on professional development through one-to-one training and hybrid working options in vibrant UK cities like Cambridge, Manchester, Bristol, and Edinburgh, employees can enjoy a balanced work-life while contributing to impactful clinical research.
StudySmarter Expert Advice🤫
We think this is how you could land Hybrid Analyst: Value & Evidence for Clinical Trials
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We think you need these skills to ace Hybrid Analyst: Value & Evidence for Clinical Trials
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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