Associate Principal Data Scientist - Early Cardiovascular, Renal and Metabolism

AstraZeneca•Boston, MA

5d•Hybrid

About The Position

AstraZeneca is seeking a highly motivated data scientist to join its Early CVRM (Cardiovascular, Renal and Metabolism) Data Science & AI Department in Cambridge, US. The team works with complex data from early clinical trials, external academic collaborations, and exploratory studies, including omics, imaging, and clinical measurements. The role applies digital, data science, and AI approaches to support scientific breakthroughs, drug discovery, and development. The position involves analyzing exploratory biomarker and other complex datasets, validating existing drug targets, identifying novel targets and biomarkers, and integrating sources such as genetics, dynamic omics, clinical observations, and electronic health records. The role also collaborates with internal and external partners to access and evaluate data, and translates data-driven insights into findings that support drug discovery and development. The role is based in Cambridge, US. AstraZeneca describes a collaborative working approach that includes face-to-face office engagement three days a week, while allowing flexibility to balance personal and work commitments.

Requirements

Strong quantitative background with a PhD or equivalent experience in bioinformatics, systems biology, statistics, data science, or another quantitative science.
Six years of experience.
Profound knowledge of cardiovascular, renal, or metabolism disease biology, with experience analyzing associated molecular and exploratory clinical data.
Experience using computational, statistical, and machine learning techniques to manipulate, visualize, and analyze large, complex datasets.
Proficiency in programming languages such as R and Python.
Experience with Unix/Linux and shell scripting.
Excellent consulting, communication, and presentation skills.

Nice To Haves

Understanding of the biological systems and signaling involved in cardiometabolic disease.
Knowledge of statistical data analysis, including experimental design, linear and 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.

Responsibilities

Analyze 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 analyze 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.