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Senior Epidemiologist, Precision Health
2 months ago
The position
The department of Computational Precision Health (CPH) is being established to support decision-making and drug discovery in Research & Early Development. We are looking for a highly motivated and skilled epidemiologist to drive studies and insights using multimodal Real-World Data.
You will contribute with your in-depth knowledge of epidemiology and your analytical skills to a team of research data scientists and epidemiologists that, together with stakeholders from across the organisation, create actionable evidence from real-world data. Our data comes from academic-industry partnerships, in-licensed databases of electronic medical records or health insurance claims, observational studies, and many other sources.
Your main responsibilities will be to:
Provide epidemiological evidence and expertise to a broad range of stakeholders across the organisation, and externally with Contract Research Organisations and academic institutions.
Conduct varied and diverse tasks within pharmaco- and classical epidemiology, including guidance and contribution to study designs and analytical methods.
Identify and determine efficient data collection approaches and data sources relevant for the questions being investigated.
Design analyses that will be conducted in collaboration with data scientists based on the data from external collaborations, Novo Nordisk observational studies, or from external commercial data sources.
Drive epidemiological synopsis or study protocol creation.
Participate in working groups requiring pharmacoepidemiology expertise.
Communicate your results and findings in a clear and convincing way both orally and in reports, and peer reviewed scientific papers.
The role requires interaction with various stakeholders – either when communicating epidemiological considerations and results to internal stakeholders or through contact with research organisations, commercial partners and academic institutions involved in analysis of epidemiological data. It is essential that you can use your interpersonal skills to achieve results together with others, both internally and when working with external project teams.
You will be given the opportunity to execute insights and studies in Python and R in collaboration with the department’s data scientists which will help you maintain or develop your programming skills as an epidemiologist.
Qualifications
To succeed in this role, you will present the relevant education; MD, DVM, or hold a master’s degree in epidemiology, public health, life sciences, biostatistics, or equivalent. You will thrive in a role where you have responsibility and work proactively and independently. Ambition and innovation are the skills we look for in the right candidate.
You will have:
Excellent communication skills together with interest in co-operating with stakeholders across the globe
Prior work experience within pharmacoepidemiology from the pharmaceutical industry or consulting
Solid hands-on experience with analytical epidemiological projects and real-world data.
Experience in project management and cross-functional stakeholder management.
Fluent English, both written and spoken.
Additionally, we would welcome, not essentially but preferably, PhD degree in a relevant field with complementing experience working on pre-clinical projects in the pharmaceutical industry.
Desirable would also be prior experience in chronic diseases such as diabetes, obesity, cardiovascular disease or kidney disease together with demonstrated proficiency in programming skills in Python and R.
About the department
The department of Computational Precision Health (CPH) has presence in Mølåv/Hellerup (Denmark) and London (UK). CPH is part of the Artificial Intelligence and Digital Research (AIDR) area, which sits within the wider Digital Science & Innovation (DSI) organisation. DSI is supporting the digital journey across all our therapy areas in Novo Nordisk Research and Early Development. The CPH department focuses on insights and causal relationships from real-world data through advanced statistical and machine learning methods. We analyse many different data sources to generate evidence to support drug discovery, preclinical and early clinical drug development projects.
