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Job Function:

Data Analytics & Computational Sciences

Job Sub Function:

Data Science

Job Category:

Scientific/Technology

All Job Posting Locations:

Cornellà de Llobregat, Barcelona, Spain, Madrid, Spain

Job Description:

Johnson and Johnson Innovative Medicine (JJIM) is recruiting a highly motivated Senior Scientist to develop robust, scalable frameworks for target identification and prioritization by integrating and quantifying multiomics and human genetics evidence. This role will play a critical part in enabling data driven target decisions and accelerating translation from discovery to clinical development by delivering fit-for-purpose, interpretable evidence to project teams.  

  

The successful candidate will work at the interface of biology, data science, and translational research, collaborating closely with therapeutic area teams, data/computational scientists, and disease geneticists. This is an onsite role based in Spain, with Madrid as the primary location and Barcelona as a secondary option. Hybrid and fully remote arrangements may be considered on a case-by-case basis.  

  

Key Responsibilities 

• Develop and implement frameworks for systematic target identification and prioritization, integrating multiomics, human genetics, and functional data.  

• Quantify and synthesize multiomics evidence (e.g., genomics, transcriptomics, proteomics, single-cell data, QTLs, real-world and clinical datasets) to support target nomination and validation decisions.  

• Establish evidence scoring and prioritization schemes that assess target–disease association, biological plausibility, safety, and translational relevance.  

• Apply causal inference, network biology, and integrative analytics to link targets, pathways, biomarkers, and disease phenotypes.  

• Partner with therapeutic area teams to translate analytical outputs into actionable insights for portfolio and program decision making.  

• Contribute to the design and evolution of reusable analytical pipelines, platforms, and best practices for target evaluation.  

• Clearly communicate complex multiomics results to diverse stakeholders through high quality written reports, visualizations, and presentations.  

 

Required Qualifications 

• PhD in Computational Biology, Bioinformatics, Genetics, Systems Biology, Biostatistics, or a related quantitative life science field, with postdoctoral or equivalent industry experience.  

• Strong background in multiomics data integration and human disease biology.  

• Demonstrated experience in target identification, target prioritization, or translational evidence generation in a drug discovery or biomedical research setting.  

• Solid understanding of human genetics and multi-omics (e.g., GWAS, rare variant analysis, xQTL, colocalization, Mendelian randomization, differential expression, and differential abundance analyses).  

• Proficiency with data analysis and modeling tools (e.g., R, Python) and experience working with largescale biological datasets.  

• Ability to work independently while thriving in highly collaborative, cross functional teams.  

  

Preferred Qualifications 

• Experience supporting clinical or late-stage discovery decision making using omics or real-world data.  

• Familiarity with AI/ML methods applied to biological inference or target prioritization.  

• Prior exposure to therapeutic area discovery pipelines (e.g., neuroscience, oncology, immunology).  

• Track record of scientific publications, internal impact, or platform development.  

  

Why This Role  

This position offers the opportunity to shape foundational target identification and prioritization frameworks that directly impact portfolio strategy and clinical success, working at the cutting edge of multiomics, human genetics, and data driven drug discovery. 

 

 

Required Skills:

 

 

Preferred Skills:

Advanced Analytics, Business Intelligence (BI), Coaching, Collaborating, Critical Thinking, Data Analysis, Database Management, Data Privacy Standards, Data Reporting, Data Savvy, Data Science, Data Visualization, Econometric Models, Process Improvements, Technical Credibility, Technologically Savvy, Workflow Analysis