FAQs
What is the primary focus of the Data Analyst role in Healthcare Analytics?
The primary focus of the Data Analyst role in Healthcare Analytics is to utilize data and analyses from the healthcare sector to address challenges for clients, such as optimizing costs while improving quality and automating fraud detection using AI/ML.
What qualifications are required for this position?
A very good Master's or Ph.D. degree in a business-related field with a focus on Data Analytics/Science or in a STEM field is required, along with at least 2 years of relevant work experience in Data Analytics/Science, Business Intelligence, or consulting.
What programming knowledge is necessary for this role?
Basic knowledge of at least one programming language, such as Python or SQL, is necessary for this role.
Is experience in the healthcare sector required?
While experience in the healthcare sector, such as with health insurance or service providers, is beneficial, it is not strictly required.
What tools should the candidate be familiar with?
Candidates should have basic knowledge of at least one BI tool, such as Tableau or PowerBI.
What kind of work environment can I expect?
The work environment is collaborative, with a strong emphasis on teamwork, and it involves close interaction with stakeholders from various sectors, including health insurance and healthcare providers.
How much travel is expected for this position?
The role requires a willingness to travel up to 80% of the time.
What soft skills are important for this role?
Excellent problem-solving skills, entrepreneurial spirit, initiative, creativity, and strong communication skills in both German and English are important for this role.
Will I have opportunities to further develop my skills?
Yes, there are numerous opportunities to continue learning and advancing your skills, especially in data analytics and AI/ML within the healthcare context.
What type of projects will I be working on?
You will work on projects involving quantitative analyses, development of data-driven solutions, and building ML/AI models, while collaborating with data engineers and consulting teams.