Bioinformatics Scientist - III (Senior)

Contractor, Complex Disease Genetics

The Complex Disease Genetics (CDG) group within ***’s Data, AI and Genome Sciences (DAGS) Department is seeking a motivated scientist to support our Cambridge-based research initiatives in complex disease genetics. We welcome applications from scientists with a graduate degree (PhD or equivalent) in statistical genetics, genetic epidemiology, computational biology, biostatistics, or a related quantitative field with hands-on experience analyzing human genetics and multi-omics data.

• has invested in external partnerships and collaborative initiatives to accelerate innovation in drug discovery and development. The CDG group leverages large-scale data resources, such as FinnGen, the Alliance for Genomic Discovery, Our Future Health, UK Biobank Pharma Proteomics Project, Open Targets, and other public and proprietary datasets, to advance ***’s drug development pipeline through human genetics. The candidate will contribute to the analysis of these large-scale datasets to support target identification and validation and the implementation of precision medicine strategies across therapeutic areas.
  
  

In this exciting role, you will:

Perform statistical genetics analyses for target discovery and validation using human genetics and multi-omics data

Support the development, implementation, and maintenance of analytical pipelines for reproducible genetic and genomic data analysis

Conduct genetic association analyses using large-scale biobank data (e.g., UK Biobank, FinnGen, Our Future Health, Alliance for Genomic Discovery)

Integrate and analyze public and proprietary genetic association summary statistics and conduct meta-analyses

Perform post-GWAS analyses to help elucidate causal mechanisms and prioritize gene targets (e.g., fine mapping, colocalization, Mendelian Randomization, TWAS, polygenic risk prediction)

Assist in integrating genetic association findings with multi-omics data (e.g., RNA-seq, ATAC-seq, QTLs) to support target prioritization

Stay current with new methods in statistical genetics and participate in evaluating and implementing emerging analytical techniques

Collaborate with wet-lab biologists, disease area experts, and data scientists to support research and patient stratification strategies

Required Experience and Skills:

PhD (or equivalent) in statistical genetics, genetic epidemiology, population genetics, computational biology, bioinformatics, biostatistics, epidemiology, or a related quantitative discipline, with a minimum of 5 year of postdoctoral or equivalent research experience in complex disease genetics

Research experience in human genetics, genomics, or related analysis, including genome-wide association studies (GWAS) and/or multi-omics analysis

Familiarity with analytical pipelines and best practices for reproducibility and scalability in genetic data analysis

Proficiency in programming languages commonly used in statistical genetics (e.g., R, Python, etc.)

Experience working with large-scale datasets in could-based computing and high-performance computing environments

Strong communication and interpersonal skills, with the ability to work effectively in multidisciplinary teams

Preferred Experience and Skills:

Experience with molecular phenotypes, such as transcriptomics or proteomics

Interest or background in cardiovascular/metabolic diseases, immunology, neuroscience, or other complex diseases

Experience with AI/ML methodology and/or application to genetics and omics analysis.

Note:

• Please add publication on the resume.
• Onsite role at Cambridge, MA.
• Do not submit candidates who are looking for remote.
• Do not submit candidates with just BS/MS.
  
  

Manager would like to consider people with 5 year post PhD experience. Please only submit these candidates. Anyone with less than 5 year relevant experience with be rejected.

Looking for strong experience with multi-omics data analysis and integration (e.g., RNA-Seq, single-cell RNA-Seq, genotype, spatial transcriptomics, OLINK).

• Proficiency in R, Python, and Bash, with the ability to establish best practices for reproducible data analyses.
• Experience with high-performance computing (HPC) systems and AWS Cloud Computing (e.g., IAM, S3 buckets).
• Most important, someone who can work oniste. No hybrid or remote.
• We would like to have someone with at least five years of experience after PHD.
• Our team specifically is required to be on site.
• Looking for multi omics experience.
• Looking for both bulk and single cell experience.
• Some spatial transcriptomics would be nice.
• So Olink, some proteomics experience and genotype data experience.
• Candidate will be working on the External datasets.
• Nonlocal is Ok as long as they are able to relocate at the own expense for the start date.
  
  

Key Skills:

• Required expertise with multi-omics data analysis and integration (e.g., RNA-Seq, single-cell RNA-Seq.
• Transcriptomics analysis.
• Proficiency in R and Bash.
• High-performance computing (HPC) systems and AWS Cloud Computing (e.g., IAM, S3 buckets).