Sr. Scientist, Bioinformatics (Computational Precision Immunology)
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Job Description
The Precision Genetics group within the Data and Genome Sciences Department is seeking a Senior Scientist to join our Computational Precision Immunology team in Cambridge, MA. We are looking for a skilled data scientist with extensive experience in companion diagnosis (CDx) development based on multi-modal and multi-scale data analyses.
Key Responsibilities:
Biomarker Identification and Patient Stratification: Integrate multi-modal omics datasets to develop biomarkers for the patient stratification
Clinical Data Analysis: Analyze the multi-omics data from internal randomized clinical trials to develop the CDx of immunology clinical trials
Real World Data Analysis: Integrate Real World Data with machine learning methods for the CDx validation
Collaborative Research: Partner closely with Clinical Research scientists to develop tailored strategies for the CDx development
Emerging Methods: Stay at the forefront of novel methodologies in computational immunology for the precision medicine
Data Set Identification: Proactively identify datasets of autoimmune diseases from public, internal, and proprietary sources through collaborative efforts
Project Management: Manage complex projects, proactively identifying challenges and forecasting timelines for key deliverables to meet pipeline objectives
Effective Communication: Present findings to project teams, internal stakeholders, and the broader scientific community through internal documentation, presentations, and publications in leading journals
Qualifications:
Education:
Ph.D. in Computational Biology or a related field with relevant research experience listed below
Required Experience and Skills:
Proven track record of three (3) or more years in multi-omics-based patient stratification, real world data analysis and CDx development
Strong conceptual understanding of generative, discriminative, and contrastive machine learning methods for the feature optimization
Extensive hands-on experience with Real World Data extraction, cleaning and analysis
Fundamental knowledge of multi-omics data analysis and integration (e.g. RNASeq, single-cell RNASeq, OLINK)
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.
Preferred Experience and Skills:
Good understanding of auto-immune disease biology
Experience in statistical and population genetics principles
#EligibleforERP
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