Senior Scientist, Data Science

AstraZeneca
AstraZeneca logo
Location
US - Waltham - MA
Job Type
Full-time
Posted
October 30, 2026
Salary Range
$119k - $178k USD

Job Description

We are seeking a highly motivated Senior 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).
  • Data Experience: Minimum 2 years of experience working with large patient-derived ‘omics datasets and clinical data to identify disease-associated pathways, candidate therapeutic targets, and biomarkers.
  • Proteomics Experience: Minimum 2 years of experience developing reproducible pipelines for proteomics preprocessing, QC, harmonization, and performing proteome-wide association studies.
  • Perturbation Analysis Experience: Working experience designing and analyzing CRISPRi perturbation studies, integrating splicing, transcriptomic, and proteomic evidence to resolve molecular mechanisms.
  • 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).
  • 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

We are seeking a highly motivated Senior 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).
  • Data Experience: Minimum 2 years of experience working with large patient-derived ‘omics datasets and clinical data to identify disease-associated pathways, candidate therapeutic targets, and biomarkers.
  • Proteomics Experience: Minimum 2 years of experience developing reproducible pipelines for proteomics preprocessing, QC, harmonization, and performing proteome-wide association studies.
  • Perturbation Analysis Experience: Working experience designing and analyzing CRISPRi perturbation studies, integrating splicing, transcriptomic, and proteomic evidence to resolve molecular mechanisms.
  • 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).
  • 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!

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 $118,870.40 - $178,305.60. 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

28-Oct-2026

Closing Date

03-Nov-2026Our 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.

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Frequently Asked Questions

Where is this role located?
The position is located in Waltham, Massachusetts, US at AstraZeneca. The listing does not specify a hybrid or remote work-mode policy, though it notes a planned company move to Kendall Square/Cambridge in 2026.
What qualifications and experience are required for this position?
Candidates need a Ph.D. in Bioinformatics, Computational Biology, Data Science, Epidemiology, or a related field (0–2 years post-grad), plus 2+ years analyzing large patient-derived 'omics/clinical datasets and developing proteomics pipelines. Strong Python or R skills, experience with CRISPRi perturbation studies, NGS data analysis, statistical analysis, and machine learning fundamentals are also required.
What are the key responsibilities of a Senior Scientist in Data Science?
You will execute and maintain biobank analysis pipelines, apply analytical methods and machine learning to identify targets and biomarkers, collaborate with wet-lab scientists on MOA studies, generate data visualizations, contribute computational insights to biomarker strategies, present findings in team meetings, and stay updated on bioinformatics tools.
What is the salary range for this role?
The annual base pay ranges from $118,870.40 to $178,305.60.
What benefits does AstraZeneca offer for this role?
AstraZeneca provides a Total Reward program featuring base salary, short-term incentive bonuses, and equity-based awards. Benefits include qualified retirement programs, generous paid time off (vacation, holidays, leaves), and comprehensive health, dental, and vision coverage.
What is the application deadline for this job posting?
The job was posted on October 28, 2026, and the closing date for applications is November 3, 2026.

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Job Information

Source: manual
AI Relevance: 92/100 (Highly relevant)
Remote Type: onsite
Skills & Tags:
astrazeneca machine learning bioinformatics computational biology genomics statistical genetics GWAS data science oncology clinical

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