Senior Scientist, Inflammation — Integrative Computational Biology

Boehringer Ingelheim GmbH; Ridgefield (CT); On-site; USD 141,000 - 211,000; Full time

Job summary

The Department of Computational Innovation (CI) at Boehringer Ingelheim is seeking a highly motivated Senior Scientist to join the CI Inflammation team.

The successful candidate will partner with CI Leads and cross-functional project teams to advance the Immunology & Respiratory drug portfolio from Pre-LO through Candidate Selection, generating timely computational evidence to strengthen disease biology understanding and patient stratification.

Qualifications

• Ph.D. in Bioinformatics, Computational Biology, Immunology, or related field with at least 2 years of postdoc experience.
• Experience with large-scale omics data and translational biology in pharma preferred.
• Strong scientific communication and cross-functional collaboration skills.

Responsibilities

• Partner with CI Leads to develop evidence packages informing PreLO to Candidate Selection milestones.
• Generate and document multi-omics and AI/ML analyses for disease biology and MoA evaluation.
• Interpret results and present concise, decision-relevant insights to multidisciplinary teams.
• Contribute to the scientific integration and decision-making across projects.

Skills

Bioinformatics expertise

Data analysis

Scientific communication

Collaborative teamwork

Independent work

Education

Ph.D. in Bioinformatics, Computational Biology, Immunology, Molecular Biology or related field

Tools

Python

R

scRNA-seq

Spatial transcriptomics

Proteomics

Job description

The Department of Computational Innovation (CI) at Boehringer Ingelheim is seeking a highly motivated Senior Scientist to join the CI Inflammation team.

The successful candidate will partner with CI Leads and cross-functional project teams to advance the Immunology & Respiratory drug portfolio from Pre-LO through Candidate Selection, generating timely computational evidence to strengthen disease biology understanding and patient stratification.