Bioinformatics Analyst III

job summary: Join a top-tier global pharmaceutical leader in Cambridge, MA, as a Bioinformatics Analyst III in a high-impact, hybrid role. You will work closely with immunology discovery teams to advance Single-Cell Atlas initiatives, analyzing large-scale transcriptomic and epigenomic datasets to uncover crucial insights into health and disease.

location: Cambridge, Massachusetts job type: Contract salary: $55.00 - 63.70 per hour work hours: 9 to 5 education: Masters

responsibilities:

• Curate, harmonize, and analyze large-scale scRNA-seq and scATAC-seq datasets from internal and public sources.
• Integrate multi-modal data to support single-cell atlas construction and define tissue-specific niches.
• Develop and apply computational and AI/ML methods to classify cell states and identify regulatory networks.
• Collaborate with cross-functional stakeholders to translate biological questions into scalable computational solutions.
• Document data curation, processing, and analysis pipelines to ensure reproducibility and transparency.

qualifications:

• MS degree with 5+ years of experience, or PhD with 0+ years of experience, in a quantitative field such as Bioinformatics, Computational Biology, Computer Science, Computational Genetics, Biostatistics, AI/Machine Learning, or a related discipline
• Proficiency in Python and standard ML/data science libraries
• Experience working in HPC or cloud environments for large-scale omics datasets
• Domain knowledge in single-cell analysis, chromatin accessibility analysis, or systems immunology
• Strong attention to detail, documentation, and communication skills
• Ability to independently design, execute, and troubleshoot computational workflows

Preferred Technical Skills:

• Experience with NumPy, Pandas, Scikit-learn, Matplotlib, and Seaborn
• Familiarity with TensorFlow and/or PyTorch
• Proficiency with Git for version control and collaboration
• Hands-on experience with single-cell analysis tools such as Scanpy, Seurat, or Bioconductor
• Exposure to multi-modal integration methods such as CITE-seq, ATAC-seq, or spatial transcriptomics

Additional Technical Skills (a plus):

Required:

• MS degree with 5+ years of experience, or a PhD in Bioinformatics, Computational Biology, Computer Science, or a related quantitative discipline.
• Strong proficiency in Python and standard data science workflows.
• Proven experience analyzing single-cell transcriptomics and epigenomics data (scRNA-seq and scATAC-seq).
• Hands-on experience with single-cell atlas construction and multi-modal data integration.
• Experience working in HPC or cloud environments for large-scale omics datasets.

Preferred:

• Experience with Scanpy, Seurat, NumPy, Pandas, and Scikit-learn.
• Familiarity with cell type annotation, clustering, trajectory inference, and Git version control.

skills: MS-EXCEL

Equal Opportunity Employer: Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other legally protected group status.

At Randstad, we welcome people of all abilities and want to ensure that our hiring and interview process meets the needs of all applicants. If you require a reasonable accommodation to make your application or interview experience a great one, please contact HRsupport@randstadusa.com.

Pay offered to a successful candidate will be based on several factors including the candidate's education, work experience, work location, specific job duties, certifications, etc. In addition, Randstad offers a comprehensive benefits package, including: medical, prescription, dental, vision, AD&D, and life insurance offerings, short-term disability, and a 401K plan (all benefits are based on eligibility).

This posting is open for thirty (30) days.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

,

• Curate, harmonize, and analyze large-scale scRNA-seq and scATAC-seq datasets from internal and public sources.
• Integrate multi-modal data to support single-cell atlas construction and define tissue-specific niches.
• Develop and apply computational and AI/ML methods to classify cell states and identify regulatory networks.
• Collaborate with cross-functional stakeholders to translate biological questions into scalable computational solutions.
• Document data curation, processing, and analysis pipelines to ensure reproducibility and transparency.

MS-EXCEL