Graduate Data Science Intern Jobs in Massachusetts

We are seeking a highly motivated Scientist to join a newly formed, dynamic team within early oncology R&D. The successful candidate will leverage their data science expertise in mining large datasets to drive our efforts in target identification, mechanism of action (MOA) studies, and biomarker strategy development, with a particular focus on analyses related to the function and aging of the immune system.

At AstraZeneca, you'll have the opportunity to make a significant impact on the future of healthcare while working in a collaborative environment at the cutting edge of research. The ideal candidate will thrive in this setting, contributing to our growth trajectory as we build our evolving team.

Key Responsibilities:

• Execute and Maintain Pipelines: Process and analyze large-scale biobank datasets, human population data, and in-vitro biological data using established analysis pipelines.

• Analytical Support: Apply analytical methods and machine learning algorithms to help identify potential therapeutic targets and biomarkers.

• Cross-Functional Collaboration: Partner with wet-lab scientists to analyze experimental results for target identification and Mechanism of Action (MOA) studies.

• Data Visualization: Generate high-quality visualizations and reports to communicate findings to the project team.

• Strategic Contribution: Provide high-quality data and computational insights that contribute to the development of biomarker strategies.

• Team Participation: Actively participate in team meetings, presenting data-driven insights to help the group meet project milestones.

• Continuous Learning: Stay current with the latest developments in data science and bioinformatics tools.

Qualifications:

• Education: Ph.D. in Bioinformatics, Computational Biology, Data Science, Epidemiology, or a related field (0-2 years post-graduate experience); or MS with 2-4 years of experience; or BS with 4+ years of relevant experience.

• Data Experience: Minimum 2 years of experience working with large-scale biological or population datasets, preferably including experience analyzing immune system aging/function within the context of human and/or mouse data.

• Coding Proficiency: Strong proficiency in Python or R.

• Technical Knowledge: Solid understanding of statistical analysis and foundational machine learning techniques.

• Genomics Foundation: Hands-on experience with NGS data analysis (e.g., RNA-seq, DNA methylation, ChIP-seq, or ATAC-seq).

• Multi-omics Interest: Experience with, or a strong desire to learn, proteomic data analysis and multi-omic data integration.

• Operational Skills: Excellent problem-solving skills, attention to detail, and the ability to manage multiple tasks in a fast-paced environment.

• Communication: Ability to clearly present data and technical workflows to a multidisciplinary team.

Desired Skills and Attributes:

• Prior experience or familiarity with biomarkers of immune system aging/function.

• Prior experience or internship in the pharmaceutical or biotechnology industry.

• Prior experience running large-scale association testing (e.g., genome-wide association studies [GWAS], epigenome-wide association studies [EWAS], proteome-wide association studies).

• Familiarity with methods in statistical genetics (e.g., Mendelian randomization, fine mapping, colocalization).

• Familiarity with machine learning analysis architectures (e.g., random forest, gradient boosting, transformers).

• Familiarity with public biological databases (e.g., GTEx, TCGA), epidemiological cohort data (e.g., TOPMed cohorts), or biobanks (e.g., UK Biobank, FinnGen).

• Ability to apply integrated generative protein design pipelines - from target-conditioned backbone generation through sequence design to computational fold validation - to support the development of novel therapeutic biologics with optimized specificity and developability properties.

• Working knowledge of computational histology pipelines incorporating modern deep learning approaches - including self-supervised and weakly supervised learning (MIL, DINO) and histopathology foundation models (e.g. UNI, CONCH) - to enable scalable, label-efficient classification of complex tissue phenotypes.

• Familiarity or prior experience with agentic AI in the context of analysis code pipeline development and biological analysis.

• Evidence of scientific contribution through publications, posters, or GitHub repositories.