Senior Scientist – Bioinformatics
| Company | Merck |
|---|---|
| Location | Cambridge, MA, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | PhD |
| Experience Level | Senior |
Requirements
- Ph.D. in Computational Biology or a related field
- Proven track record in multi-omics-based patient stratification and CDx development
- Fundamental knowledge of multi-omics data analysis and integration (e.g. RNASeq, single-cell RNASeq, spatial transcriptomics, OLINK)
- Strong conceptual understanding of generative, discriminative, and contrastive machine learning methods for the feature optimization
- Proficiency in coding using R and Python, with the ability to establish best practices for reproducible data analyses
- A collaborative and self-motivated individual with a strong work ethic, ability to work in a dynamic environment and able to manage multiple objectives in parallel and adapt to changing priorities
- Excellent written and verbal communication skills
Responsibilities
- Proactively identify datasets of autoimmune diseases from public, internal, and proprietary sources through collaborative efforts
- Partner closely with Clinical Research scientists to develop tailored strategies for the CDx development of immunology clinical trials
- Integrate genetic and genomic datasets to develop biomarkers for the patient stratification
- Collaborate with the Genome Sciences group to identify biomarkers from spatial transcriptomics on patient-derived samples
- Analyze the multi-omics data from internal randomized clinical trials to develop and validate the CDx
- Stay at the forefront of novel methodologies in computational immunology for the precision medicine
- Manage complex projects, proactively identifying challenges and forecasting timelines for key deliverables to meet pipeline objectives
- Present findings to project teams, internal stakeholders, and the broader scientific community through internal documentation, presentations, and publications in leading journals
Preferred Qualifications
- Experience in the process and analysis of real-world data
- Good understanding of auto-immune disease biology
- Experience in statistical and population genetics principles