Our client is a rapidly growing early-stage biotech company based in San Diego, CA, specializing in delivering cutting-edge multi-omics services to accelerate drug development and advance personalized medicine. Leveraging state-of-the-art mass spectrometry, advanced biocomputational tools, and diverse human health data, they enable biomarker discovery at unparalleled speed and scale. Their mission is to deepen understanding of human biology to align biological targets, patients, and drug therapies more effectively. Their platform technology enhances treatment precision and efficacy, transforming and improving countless lives.
About the Role
We are seeking a dedicated Data Scientist to contribute to biomarker discovery and R&D through the analysis of clinical and multi-omics data. Reporting to a senior data scientist, this role focuses on solving structured problems and generating insights from metabolomics, proteomics, genomics, and clinical datasets.
The ideal candidate brings experience in data mining, statistical analysis, model building, algorithm development, and contributing to biomarker discoveries. You will collaborate with engineers, chemists, biologists, and clinicians, both internally and externally, to derive impactful insights that advance drug development and healthcare innovation.
Responsibilities
- Analyze datasets, mine databases, and develop code in line with company best practices to enhance project outcomes.
- Apply rigorous statistical and machine learning techniques to high-dimensional omics and clinical data for meaningful insights.
- Collaborate with internal and external scientists and clinicians to address scientific and medical challenges through critical analysis.
- Clearly communicate methodologies, data insights, and findings to collaborators and clients.
Experience and Qualifications
- Education: MS or PhD in Biostatistics, Bioinformatics, Computational Biology, Bioengineering, Data Science, Applied Math, Physics, or related Life Sciences field.
- Experience: 2+ years in statistical analysis, data mining, modeling, and coding.
- Skills:
- Expertise in statistical methods and foundational machine learning algorithms.
- Proficiency in Python or R for clean, product-level code development.
- Familiarity with CI/CD, clean code principles, testing, and version control (e.g., Git).
- Experience with SQL, cloud computing (e.g., AWS), and clinical/epidemiological data analysis (preferred).
- Knowledge:
- Strong domain knowledge in life sciences with a practical understanding of scientific contexts.
- Ability to select appropriate data analysis approaches tailored to scientific questions.
- Communication: Excellence in visualizing data, creating presentations, and authoring scientific reports.
- Attributes: Self-motivated, dedicated, and accountable.
