Posted 6 days ago

Scientist, Data Science

4314 AstraZeneca Pharmaceuticals LP Company United States of America, US - Waltham - MA
Onsite Full Time

Job 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. 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. As AstraZeneca continues to put patients at the forefront of our mission, we are excited for our move to Kendall Square/Cambridge in 2026. Find out more information here: Kendall Square Press Release Ready to join us on this mission? Apply now! If you’re curious to know more, please contact Bobbi Poole, our Talent Acquisition Partner. Competitive remuneration and benefits apply We offer a competitive Total Reward program including a market driven base salary, bonus and long-term incentive. We have a generous paid time off program and a comprehensive benefits package. The annual base pay for this position ranges from $91,008.80 - $136,513.20. Our positions offer eligibility for various incentives—an opportunity to receive short-term incentive bonuses, equity-based awards for salaried roles and commissions for sales roles.

Benefits

offered include qualified retirement programs, paid time off (i.e., vacation, holiday, and leaves), as well as health, dental, and vision coverage in accordance with the terms of the applicable plans. Date Posted 06-Aug-2026 Closing Date 29-Aug-2026 Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and employees. In furtherance of that mission, we welcome and consider applications from all qualified candidates, regardless of their protected characteristics. If you have a disability or special need that requires accommodation, please complete the corresponding section in the application form. AstraZeneca is a global, science-led, patient-focused biopharmaceutical company. We focus on discovering, developing and commercialising prescription medicines for some of the world’s most serious diseases. But we are more than one of the world’s leading pharmaceutical companies. At AstraZeneca, we’re dedicated to being a Great Place to Work. Where you are empowered to push the boundaries of science, challenge convention and unleash your entrepreneurial spirit. To embrace differences and take bold actions to drive the change needed to meet global healthcare and sustainability challenges. There is no better place to make a difference in medicine, patients, and society. An inclusive culture where you will connect different thinking to generate new and valuable opportunities. Where you will find a commitment to lifelong learning, growth and development for all. Our Inclusion & Diversity (I&D) mission is to create an inclusive and equitable environment where people belong, using the power of our diversity to push the boundaries of science to deliver life-changing medicines to patients. Inclusion and diversity are fundamental to the success of our company, because innovation requires breakthrough ideas that only come from a diverse workforce empowered to challenge conventional thinking. We’re curious about science and the advancement of knowledge. We find creative ways to approach new challenges. We’re driven to make the right choices and be accountable for our actions. As an organisation centred around what makes us human, we put a big focus on people. Across our business, we want colleagues to wake up excited about their day at the office, in the field, or in the lab. Along with our purpose to bring life-changing medicines to people across the globe, we have a promise to you: to help you realise the full breadth of your potential. Here, you’ll do work that has the potential to change your life and improve countless others. And, together with your team, you’ll shape a culture that unites and inspires us every day. This is your life at AstraZeneca.

Skills and functions

  • Data Science
  • Human Resources
  • Machine Learning
  • Python