Senior Data Scientist

GRIFOLS, S.A.•CA-San Carlos, MN

3d•$165,000 - $225,000•Onsite

About The Position

The Senior Data Scientist executes clinical biostatistics and machine learning projects supporting Grifols stakeholders, including Alkahest Data Science, Alkahest Proteomics, Plasma R&D, and Biosupplies. The role leads and advises on data analysis for the setup, evaluation, and interpretation of longitudinal and high-dimensional omics and real-world evidence datasets. It specializes in longitudinal multivariate data analysis, including linear mixed models, dimensionality reduction, prediction modeling, survival analysis, causal inference, and modern AI methods such as foundation models and multimodal learning applied to large-scale omics and real-world datasets. This position performs independent, high-impact integrative analyses of Alkahest and Grifols datasets and serves as an internal contact for pre-clinical and clinical data science and biostatistics. It contributes to cross-functional scientific projects, develops and applies advanced machine learning, deep learning, and AI methods, and may mentor other biostatisticians and data scientists. The primary role is an individual contributor. The position is based in an office environment in San Carlos, California, and frequently involves driving to site locations, with occasional travel within the United States. The estimated annual pay range is $165,000 to $225,000, and the position is eligible to participate in up to 20% of the company bonus pool. Final compensation depends on factors including education, experience, skills, location, internal equity, and market data.

Requirements

Ph.D.; alternatively, depending on assignment, a BA/BS or advanced degree with at least 12 years of advanced experience may be considered.
Typically 8 or more years of relevant experience.
Expert knowledge of scientific principles and concepts in the applicable discipline.
Solid background in statistics, machine learning, deep learning, and AI methodologies, including linear and nonlinear modeling, multivariate analysis, causal inference, network analysis, algorithm development, representation learning, foundation models, multimodal learning, and model interpretability.
Experience applying foundation models, transfer learning, and modern AI approaches to large-scale biomedical, clinical, omics, imaging, and real-world datasets.
Excellent statistical programming skills in Python and R.
Demonstrated experience analyzing proteomics combined with other large-scale data types, including genomics, real-world data, RNA-seq, and imaging.
Significant experience working with preclinical or clinical study data.
Ability to exercise independent judgment in developing methods, techniques, and evaluation criteria.
Ability to explain difficult concepts and influence others.
Excellent written and oral communication skills.
Ability to draw conclusions from novel research and create solutions to critical new problems.

Responsibilities

Execute cross-functional scientific experiments and projects that contribute to the overall direction of the department or discipline.
Apply professional concepts to develop methods for problems such as identifying novel therapeutic targets and biomarkers for disease diagnosis, risk, and progression prediction.
Ensure the completeness, critical evaluation, correctness, and integrity of datasets and analyses.
Execute independent integrative analyses of Alkahest and Grifols datasets that are critical and high impact.
Serve as an internal contact for pre-clinical and clinical data science and biostatistics.
Contribute to developing and applying machine learning, deep learning, and AI technologies, including foundation models and multimodal approaches for biological, clinical, and real-world data.
Build cross-functional networks with peers and key decision-makers and serve as an internal spokesperson for the function.
Attend and participate in national and international scientific meetings.
May mentor other biostatisticians and data scientists to improve the quality and quantity of their work.
Demonstrate autonomy in selecting methods, techniques, and evaluation criteria.
Achieve measurable accomplishments that advance the scientific or technical field.
Author abstracts, posters, and peer-reviewed papers; contribute regularly to scientific literature and begin gaining external recognition.
Monitor and evaluate the completion of tasks and projects.