Quantitative Insights Lab Intern

Job Description

This internship opportunity is in the Quantitative Insights Lab of the QM&G organization which plays a key role in using innovative bioinformatic data analysis to generate actionable insights for AbbVie’s therapeutic areas by leveraging genetics and ‘omics data at scale. Under the supervision of senior scientists, the intern will compile results from large internal and external databases of functional genomic perturbation data, including CRISPR screens and Perturb-seq datasets to connect genes to molecular functions associated with diseases across Therapeutic Areas (TAs). The intern will have the opportunity to participate in cross-functional team efforts to develop a knowledge graph framework that can be used to extract meaningful insights.  

Importance: The intern will play a vital role in the creation and optimization of resources to advance the understanding of disease mechanisms through innovative data integration and representation techniques. 

Key Responsibilities: 

• Ingest existing internal and external compilations of perturbation data into a common knowledge graph 
• Integrate with other data sources in AbbVie knowledge graphs such as genetic associations and pathways to draw connections between genes, gene perturbations, and disease mechanisms.  
• Assess predictive performance of functional screens to identify outcomes of interest such as known drug mechanisms 

Qualifications

Minimum Requirements 

• Currently enrolled in university, pursuing PhD in Computational Biology, Bioinformatics, Statistical Genetics or related field 
• Must be enrolled in university for at least one semester following the internship 
• Proficient with statistical and programing languages (R, Python) and Linux environment 
• Experience writing custom functions in R or python to statistically interrogate and visualize high dimensional biological datasets  
• Demonstrated ability to execute custom computational analysis plans leveraging novel algorithms and relevant databases 
• Evidence of independent research capability including hypothesis development, experimental design and execution, data interpretation, and problem solving 
• Very good communication skills demonstrated in inter-disciplinary teams. 
• A self-motivated individual with a strong work ethic 

Preferred Qualifications  

• Expected graduation date between December 2026 – June 2027
• Experience processing and analyzing imaging data, single cell RNA-seq, CRISPR screens or other similar NGS/genomic data 
• Experience with knowledge graph tools such as neo4j 
• Experience with database management using SQL or other tools. 
• Experience with AWS cloud computing