Computational Scientist

18-month contract

Onsite in Lexington, MA

Key Responsibilities

• Design and implement unsupervised and supervised machine learning models to identify patient subtypes and treatment-relevant disease endotypes.
• Integrate and analyze multi-modal omics datasets (particularly proteomics: Olink, SomaScan, LC-MS) with clinical and phenotypic data.
• Collaborate with cross-functional teams to interpret and translate data insights into actionable hypotheses for target discovery and validation.
• Analyze large-scale human cohort data and real-world datasets to define biomarkers and inform clinical outcomes.
• Communicate complex analytical results clearly to non-technical stakeholders (e.g., clinicians, therapeutic area leads).
• Stay current with the latest developments in explainable AI, bioinformatics, and cardiometabolic research to drive innovation.

Qualifications

• PhD in Bioinformatics, Computational Biology, Machine Learning, Genomics, or a related field with 1+ year of project experience (industry or academic)
• OR
• Master’s degree with 3+ years of relevant professional experience
• Demonstrated experience analyzing human-derived omics data, especially proteomics
• Proven track record of patient stratification, disease subtyping, or endotype discovery
• Strong proficiency in Python and/or R, including use of ML libraries and statistical modeling tools
• Hands-on experience with large-scale cohort data analysis
• Solid understanding of clinical data structure and outcomes, including real-world evidence integration
• Ability to translate complex data into meaningful insights for biological and clinical collaborators
• Excellent communication skills; capable of interfacing with biologists, clinicians, and data scientists

Preferred Qualifications

• Experience with clinical trial datasets or CDISC standards
• Background in cardiometabolic disorders, especially Type 2 Diabetes or cardiovascular disease
• Familiarity with high-performance computing and version control tools (e.g., Git)
• Publication record in peer-reviewed journals (a plus)