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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Scientist, Data Science - **Company:** AstraZeneca plc - **Location:** Waltham, MA, United States - **Salary:** $91,009.0 - $136,513.0 - **Contract:** Internship / Graduate position - **Skills:** Data Analysis, Big Data, Bioinformatics, Computational Biology, Computer Programming, Databases, Data Integration, Github, Python (Programming Language), Machine Learning, Supervised Learning, Random Forest, Deep Learning, Xgboost - **Published:** August 7, 2026 - **Apply:** https://www.jofdav.com/jobs/59134022-scientist-data-science ## About the Role 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. ## Description 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., Team Participation: Actively participate in team meetings, presenting data-driven insights to help the group meet project milestones. ## Related Videos - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Explainable machine learning explained](https://www.wearedevelopers.com/videos/589-explainable-machine-learning-explained) ## Related Articles - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [How to start an AI project for a good cause and boost your career](https://www.wearedevelopers.com/magazine/15-how-to-start-an-ai-project-for-a-good-cause-and-boost-your-career) - [Data Analyst Salary Austria](https://www.wearedevelopers.com/magazine/275-data-analyst-salary-austria) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk)