Biomarker Data Scientist

Bayer•Cambridge, MA

3d•$104,480 - $156,720•Hybrid

About The Position

As Biomarker Data Scientist within the Digital Health & Real World Sciences team, you are an integral part of our DS&AI organization within R&D. You develop innovative solutions to enhance the analysis of large datasets and diverse data types, such as clinical trial data, biomarkers, digital markers, and real-world data (RWD). Your statistical expertise will contribute to cross-functional teams and ensure the appropriate and efficient use of statistical planning and analysis methods during clinical development of Bayer products. You will help bridge the gap between machine learning and clinical statistics by integrating machine learning techniques into existing clinical workflows while also pursuing novel approaches to modeling varied responses across patient subgroups.

Requirements

MSc, PhD degree in statistics, biostatistics, computer science, bioinformatics, or closely related fields with several years of experience in the pharmaceutical industry.
Strong programming skills in R and/or Python for data preprocessing, data analysis, statistical modeling, and machine learning, as well as solid SQL skills for data querying and manipulation.
Strong data analytical experience working with large and complex datasets, including data extraction, cleaning, transformation, and feature engineering in a reproducible way (e.g. using Python and/or R, SQL, and version control). Experience collaborating with data engineers on data pipelines is a plus.
Solid clinical data domain expertise:
Experience with clinical data domain standards, such as CDISC, and a solid understanding of their application in the context of randomized controlled trials (RCTs) and study designs.
Familiarity with real-world data (RWD) sources and methodologies, including the ability to integrate and analyze RWD for insights and evidence generation in life sciences.
Good understanding of software design principles and experience building reproducible analysis workflows and model pipelines.
Familiarity with modern data and ML tooling and collaborate effectively with data and ML engineers.
Comfortable using AI‑assisted coding tools to accelerate development and exploration, while retaining responsibility for code quality and analytical rigor.
Experience exposing models or analyses via simple APIs or integrating with existing applications is an advantage, but not the primary focus.
Experience developing and validating GxP‑compliant analytical code and software, contributing to protocols, statistical analysis plans, and other regulatory‑relevant documents.
Ability to work effectively independently and cross-functionally, depending on project requirements.
Outstanding problem-solving ability to resolve challenging scientific tasks.
Strong interpersonal skills role modelling Dynamic Shared Ownership behaviors combined with excellent written and verbal communications.

Nice To Haves

Minimum of 5 years practical experience as a data scientist with at least two years spent in pharma, biotech or similar sector.

Responsibilities

Perform advanced statistical analyses for clinical and biomarker studies, including causal inference, Bayesian methods, time‑series analysis, and high‑dimensional data analysis.
Execute exploratory and confirmatory biomarker analyses across clinical trial data, omics data, and real‑world data sources.
Evaluate and prototype advanced analytical methods through literature review, applied use cases, and method validation.
Support statistical evaluation strategies and analysis plans (e.g., SAPs, BEPs) for assigned studies and contribute statistical input to study protocols, including sample size calculations and methodological justification.
Develops and implements standard processes for the analysis of routine (parts of) wet and digital biomarker assessments and automates insight generation for the production of statistical reports, presentations, and publications.
Develop and maintain analytical code, pipelines, and tools to support study execution.
Create and contribute to training materials on statistical and machine learning methods for clinical data analysis
Works independently at routine and complex quantitative questions and tasks.
Act as go-to person with accountability for the incorporation of advanced statistical concepts into digital statistical solutions and develop statistics applications prototypes, including requirements
As needed, closely collaborate with scientists from various disciplines across DSAI and R&D, external software companies, and academic partners.
Oversees and ensures accurate and timely delivery of quantitative work outsourced to external providers.