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# Associate Principal Data Scientist - Early Cardiovascular, Renal and Metabolism

- **Company:** AstraZeneca plc
- **Location:** Boston, MA, United States
- **Experience:** Expert
- **Salary:** $143,323.0 - $214,984.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Big Data, Bioinformatics, Clinical Data Repository, Data Mining, Python (Programming Language), Machine Learning, Shell Script, Electronic Medical Records
- **Published:** October 28, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=601724fcabd486c3

## About the Role

* Strong quantitative background with a PhD or equivalent experience in bioinformatics, systems biology, statistics, data science or other quantitative sciences.
 * 6 years of experience
 * Profound knowledge of cardiovascular, renal or metabolism disease biology and experience with analysis of associated molecular and exploratory clinical data.

 * Experience in using computational, statistical and machine learning techniques for the manipulation, visualization, and analysis of large complex datasets.

 * Proficiency in programming languages such as R, Python and experience with Unix/Linux and shell scripting.

 * Excellent consulting, communication, and presentation skills.

Desirable Skills/Experience:

 * Understanding of the biological systems and signaling involved in cardiometabolic disease.
 * Knowledge of statistical data analysis including experimental design, linear/nonlinear models, mixed effect models, exploratory biomarkers, diagnostic analyses, applied Bayesian statistics, data mining, multivariate data analysis, and statistical learning.

 * Experience leading statistical analysis projects aligned to drug discovery.

## Description

* Analyse and interpret large, complex datasets, including exploratory biomarker data from clinical trials.
 * Validate existing drug targets using data-driven approaches.
 * Identify novel drug targets and biomarkers to support disease modulation and patient stratification.
 * Integrate and analyse big data sources, including genetics, dynamic omics, clinical observations, and electronic health records.
 * Collaborate with internal and external partners to access, assess, and apply relevant data sources.
 * Translate data-driven insights into findings that support drug discovery and development.