Senior Clinical Genetics Data Scientist

Novo Nordisk
Location
Søborg, Capital Region of Denmark, DK
Job Type
Full-time
Posted
November 13, 2026

Job Description

.

Your impact begins here

As Senior Clinical Genetics Data Scientist, you will turn human genetic evidence into clear recommendations that guide clinical development decisions. You will lead defined projects that combine trial and population genetic data with clinical outcomes and molecular measurements. Your work will help explain disease biology, understand why people respond differently to treatment and identify where patient stratification, or asset differentiation could matter. It helps teams decide with confidence, for people living with serious chronic diseases.

What you’ll bring

You are an independent, systematic scientist with sound judgement, who communicates uncertainty openly, acts on feedback and sees work through to its outcome.

  • A PhD in statistical genetics, genetic epidemiology, bioinformatics or a related quantitative discipline; or a relevant master’s degree with equivalent demonstrated research and applied experience.
  • Demonstrated independent ownership of complex human genetics projects, from study design and data preparation to robust interpretation and communication of results.
  • Strong, hands-on experience in GWAS design, execution and interpretation, including linkage disequilibrium, population structure, relatedness, multiple testing, power and validation.
  • Proficiency in R and/or Python, Linux and genetics tools such as PLINK, BCFtools, GATK, REGENIE, SAIGE or equivalent, with reproducible programming, version control, documentation and quality review.
  • Practical experience processing SNP array and WGS data, including quality control, genotype harmonisation, phasing and imputation, variant calling, filtering and functional annotation.
  • Hands-on experience conducting and interpreting pQTL analyses, including cis and trans mapping, multiple-testing control and integration with GWAS or colocalisation evidence.
  • The ability to coordinate stakeholders, manage project risks and dependencies, and present complex findings clearly in English to audiences with different technical backgrounds.

Desirable, but not essential:

Experience in clinical trial genetics, pharmacogenetics or clinical development, particularly in cardiometabolic, obesity or renal disease; rare-variant and gene-based tests, genetic risk prediction or advanced causal inference; or work with secure cloud computing, biobank-scale resources and regulated analytical environments.

What you’ll do

You will own project outcomes from the scientific question to validated results and practical recommendations.

  • Plan and run GWAS, pQTL and related analyses of disease phenotypes, clinical endpoints, biomarkers and treatment response, using trial and external cohort data.
  • Prepare SNP array and whole-genome sequencing data into analysis-ready datasets, and use fine-mapping, colocalisation and causal inference to prioritise plausible variants, genes, pathways and biomarkers.
  • Investigate pharmacogenetic associations and genetically informed subgroups, separating prognostic signals from evidence of differential treatment benefit.
  • Turn findings into decision-focused reports and presentations, with clear uncertainty and limitations, and contribute genetics content to publications and regulatory documents.
  • Build documented, version-controlled workflows, mentor colleagues through code review and feedback, and use genetic and clinical data within approved consent, governance and quality requirements.

Who you’ll work with

You will join Clinical Omics Sciences, part of Translational & Clinical Omics in Research & Development. Here, colleagues explore scientific ideas and advance them towards clinical testing, bringing together curiosity, data, technology and collaboration.

Your closest partners will be Clinical Development Teams, translational scientists, human genetics researchers, biostatisticians, data managers and data engineers. You will also coordinate agreed work with external collaborators and suppliers. You will explain your technical choices in accessible language, build alignment on the question, deliverables and decision, and speak up early when something stalls.

.

Your impact begins here

As Senior Clinical Genetics Data Scientist, you will turn human genetic evidence into clear recommendations that guide clinical development decisions. You will lead defined projects that combine trial and population genetic data with clinical outcomes and molecular measurements. Your work will help explain disease biology, understand why people respond differently to treatment and identify where patient stratification, or asset differentiation could matter. It helps teams decide with confidence, for people living with serious chronic diseases.

What you’ll bring

You are an independent, systematic scientist with sound judgement, who communicates uncertainty openly, acts on feedback and sees work through to its outcome.

  • A PhD in statistical genetics, genetic epidemiology, bioinformatics or a related quantitative discipline; or a relevant master’s degree with equivalent demonstrated research and applied experience.
  • Demonstrated independent ownership of complex human genetics projects, from study design and data preparation to robust interpretation and communication of results.
  • Strong, hands-on experience in GWAS design, execution and interpretation, including linkage disequilibrium, population structure, relatedness, multiple testing, power and validation.
  • Proficiency in R and/or Python, Linux and genetics tools such as PLINK, BCFtools, GATK, REGENIE, SAIGE or equivalent, with reproducible programming, version control, documentation and quality review.
  • Practical experience processing SNP array and WGS data, including quality control, genotype harmonisation, phasing and imputation, variant calling, filtering and functional annotation.
  • Hands-on experience conducting and interpreting pQTL analyses, including cis and trans mapping, multiple-testing control and integration with GWAS or colocalisation evidence.
  • The ability to coordinate stakeholders, manage project risks and dependencies, and present complex findings clearly in English to audiences with different technical backgrounds.

Desirable, but not essential:

Experience in clinical trial genetics, pharmacogenetics or clinical development, particularly in cardiometabolic, obesity or renal disease; rare-variant and gene-based tests, genetic risk prediction or advanced causal inference; or work with secure cloud computing, biobank-scale resources and regulated analytical environments.

What you’ll do

You will own project outcomes from the scientific question to validated results and practical recommendations.

  • Plan and run GWAS, pQTL and related analyses of disease phenotypes, clinical endpoints, biomarkers and treatment response, using trial and external cohort data.
  • Prepare SNP array and whole-genome sequencing data into analysis-ready datasets, and use fine-mapping, colocalisation and causal inference to prioritise plausible variants, genes, pathways and biomarkers.
  • Investigate pharmacogenetic associations and genetically informed subgroups, separating prognostic signals from evidence of differential treatment benefit.
  • Turn findings into decision-focused reports and presentations, with clear uncertainty and limitations, and contribute genetics content to publications and regulatory documents.
  • Build documented, version-controlled workflows, mentor colleagues through code review and feedback, and use genetic and clinical data within approved consent, governance and quality requirements.

Who you’ll work with

You will join Clinical Omics Sciences, part of Translational & Clinical Omics in Research & Development. Here, colleagues explore scientific ideas and advance them towards clinical testing, bringing together curiosity, data, technology and collaboration.

Your closest partners will be Clinical Development Teams, translational scientists, human genetics researchers, biostatisticians, data managers and data engineers. You will also coordinate agreed work with external collaborators and suppliers. You will explain your technical choices in accessible language, build alignment on the question, deliverables and decision, and speak up early when something stalls.

What you can expect here

You will be trusted to determine and execute the analytical approach for your projects, within agreed objectives and quality requirements. You will be supported to perform at your best, make your voice heard and grow through practical project experience, scientific feedback and relevant learning offerings. You will also help raise the standard for the whole team by shaping shared workflows, templates and methods.

Salary: For this role, the Annual Base Salary ranges from 651,000 to

956,900 DKK, corresponding to the level of the position. The placement within the salary range will be assessed during the recruitment process based on the candidate's skills, competencies, knowledge and relevant experience.

Incentives and Benefits: The salary package may include short-term and/or long-term incentives as well as other employee benefits based on position level, location, functional area and relevant market benchmarks.

Learn more about our Reward Philosophy here

Start now

Your impact begins today. Please apply by 27 November 2026. We review applications on an ongoing basis, so we encourage you to apply as soon as possible.

We are an equal opportunities employer and are committed to an inclusive recruitment process and equality of opportunity for all applicants.

Frequently Asked Questions

Where is this job located, and is it remote, hybrid, or on-site?
The role is based in Søborg, Capital Region of Denmark, Denmark. The posting does not specify whether the position is remote, hybrid, or on-site.
What qualifications and experience do I need?
You need a PhD in statistical genetics, genetic epidemiology, bioinformatics or a related quantitative field, or a relevant master's with equivalent experience. You also need hands-on GWAS and pQTL experience, SNP array and WGS data processing skills, and proficiency in R and/or Python, Linux, and tools such as PLINK, REGENIE or SAIGE.
What will I be doing day to day?
You will plan and run GWAS, pQTL and related analyses using trial and cohort data. You will prepare SNP array and WGS data and apply fine-mapping, colocalisation and causal inference. You will investigate pharmacogenetic associations, write decision-focused reports, and contribute to publications and regulatory documents. You will also build version-controlled workflows and mentor colleagues.
What is the salary range?
The annual base salary ranges from 651,000 to 956,900 DKK, depending on the level of the position. Placement within the range is based on your skills, competencies, knowledge and relevant experience.
Are there incentives or other benefits?
The salary package may include short-term and/or long-term incentives and other employee benefits. These depend on position level, location, functional area and market benchmarks. You can also expect learning offerings and scientific feedback.
Which team will I join, and who will I work with?
You will join Clinical Omics Sciences, part of Translational & Clinical Omics in R&D. Your closest partners will be Clinical Development Teams, translational scientists, human genetics researchers, biostatisticians, data managers and data engineers. You will also coordinate work with external collaborators and suppliers.
What is the application deadline?
Apply by 27 November 2026. Applications are reviewed on an ongoing basis, so early application is encouraged.

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Job Information

Source: manual
AI Relevance: 92/100 (Highly relevant)
Remote Type: onsite
Skills & Tags:
novo nordisk bioinformatics statistical genetics GWAS clinical

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