Senior Scientist, Data Science
AstraZeneca•Waltham, MA
•$118,870 - $178,306
About The Position
AstraZeneca is seeking a Senior Scientist to join a newly formed team within early oncology R&D. The role uses data science expertise to mine large datasets and support target identification, mechanism of action (MOA) studies, and biomarker strategy development, with a focus on immune-system function and aging. The successful candidate will work collaboratively with research teams and contribute computational insights to the development of the group and its projects.
Requirements
- Ph.D. in Bioinformatics, Computational Biology, Data Science, Epidemiology, or a related field.
- At least 2 years of experience working with large patient-derived omics datasets and clinical data to identify disease-associated pathways, candidate therapeutic targets, and biomarkers.
- At least 2 years of experience developing reproducible pipelines for proteomics preprocessing, quality control, and harmonization, and performing proteome-wide association studies.
- Working experience designing and analyzing CRISPRi perturbation studies and integrating splicing, transcriptomic, and proteomic evidence to resolve molecular mechanisms.
- Strong proficiency in Python or R.
- Solid understanding of statistical analysis and foundational machine learning techniques.
- Hands-on experience with NGS data analysis, such as RNA-seq, DNA methylation, ChIP-seq, or ATAC-seq.
- Experience with, or a strong desire to learn, proteomic data analysis and multi-omic data integration.
- Excellent problem-solving skills, attention to detail, and ability to manage multiple tasks in a fast-paced environment.
- Ability to clearly present data and technical workflows to a multidisciplinary team.
Nice To Haves
- Prior experience or familiarity with biomarkers of immune-system aging or function.
- Prior experience or internship in the pharmaceutical or biotechnology industry.
- Prior experience running large-scale association testing, such as GWAS, EWAS, or proteome-wide association studies.
- Familiarity with statistical genetics methods such as Mendelian randomization, fine-mapping, and colocalization.
- Familiarity with machine learning architectures such as random forest, gradient boosting, and transformers.
- Familiarity with public biological databases such as GTEx or TCGA, epidemiological cohort data such as TOPMed cohorts, or biobanks such as UK Biobank or FinnGen.
- Familiarity with or prior experience using agentic AI for analysis-pipeline development and biological analysis.
- Evidence of scientific contributions through publications, posters, or GitHub repositories.
Responsibilities
- Process and analyze large-scale biobank datasets, human population data, and in-vitro biological data using established analysis pipelines.
- Apply analytical methods and machine learning algorithms to help identify potential therapeutic targets and biomarkers.
- Collaborate with wet-lab scientists to analyze experimental results for target identification and MOA studies.
- Generate visualizations and reports to communicate findings to the project team.
- Provide high-quality data and computational insights to contribute to biomarker strategy development.
- Participate in team meetings and present data-driven insights to help meet project milestones.
- Stay current with developments in data science and bioinformatics tools.
Benefits
- Competitive market-driven base salary.
- Eligibility for short-term incentive bonuses.
- Eligibility for equity-based awards for salaried roles.
- Generous paid time off, including vacation, holidays, and leaves.
- Qualified retirement programs.
- Health, dental, and vision coverage.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Ph.D. or professional degree
