23 sep
Brightgrove
Medellín
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About the Client
A pioneering biotech company dedicated to advancing longevity and regenerative medicine. By harnessing breakthroughs in cellular biology, AI-driven analytics, and bioengineering, we develop cutting-edge solutions to enhance health and extend lifespan. Our multidisciplinary team of scientists, data engineers, and researchers collaborates to drive innovation in ageing research, biomarker discovery, and precision health technologies.
You’ll work on a platform that applies LLM-based pipelines to scientific literature, transforming it into structured knowledge that drives scientific research and insights.
Your Team
We’re looking for a Senior Data Science to join a small,
cross-functional team (2 engineers, 2 biologists) delivering value in fast, iterative cycles. You’ll work in a dynamic startup environment with shifting priorities and uncertainty. The Data Scientist will play a pivotal role in analyzing and interpreting large-scale omics datasets—particularly proteomics—to accelerate the discovery of biomarkers, biological mechanisms, and drug targets related to human aging, healthspan, and lifespan. This role focuses on building, maintaining, and optimizing computational pipelines for both longitudinal and cross-sectional analyses, supported by robust statistical validation.
The successful candidate will contribute to an AI-driven drug discovery platform by overseeing quality control, generating derived phenotypes, automating metadata curation, and integrating external datasets. This position requires strong expertise in data science, bioinformatics, and applied statistics, paired with a passion for transforming complex biological data into actionable therapeutic insights.
What's in it for you
- Interview process that respects people and their time
- Professional and open IT community
- Internal meet-ups and resources for knowledge sharing
- Time for recovery and relaxation
- Bright online and offline events
- Opportunity to become part of our internal volunteer community
Responsibilities
- Design, maintain, and optimize scalable pipelines for large-scale omics data analysis, with a strong focus on proteomics. This includes implementing and refining statistical methods for both longitudinal and cross-sectional studies.
- Automate the creation of data dictionaries from unstructured or semi-structured sources.
- Conduct rigorous quality control on high-dimensional omic and phenotypic datasets, addressing inconsistencies, missing data, and batch effects. Generate secondary phenotypes to support advanced longitudinal studies.
- Validate analytical outputs with replication, sensitivity testing, and independent checks to ensure robustness, accuracy, and biological relevance.
- Troubleshoot and fix bugs, refactor legacy code, and maintain thorough technical documentation, including user guides and annotations, to support internal reproducibility and collaboration.
- Integrate and analyze external datasets from public repositories, scientific literature, or proprietary sources, ensuring harmonization, annotation, and benchmarking against internal data.
- Communicate research findings effectively within the data science group and across interdisciplinary teams.
Skills
- Bachelor’s degree or higher in Computational Biology, Bioinformatics, Biostatistics, Data Science, or a related quantitative discipline (required).
- Hands-on experience working with high-dimensional biological data,
such as omics (e.g., proteomics, transcriptomics), phenotypic, or clinical datasets (desirable).
- Proficiency in Python and/or R, with demonstrated experience in building and maintaining data analysis pipelines.
- Strong foundation in statistics and data quality control methods, particularly in the context of longitudinal and cross-sectional studies.
- Excellent problem-solving skills, attention to detail, and the ability to work independently as well as collaboratively in a fast-paced research setting.
- Clear communication skills, with the ability to document processes thoroughly and present findings to cross-functional teams.
- Additional expertise—such as published work or domain knowledge in aging biology, molecular biology, medicine, or drug discovery—is a strong plus.
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