Scientist, Computational Biology

About the role

FL103 is seeking a highly motivated and experienced computational biologist to join our early-stage biotech company. The successful candidate will be a driven, experienced scientist who is enthusiastic about developing state-of-the-art techniques to pioneer the detection of cellular dynamics from minimally invasive biospecimens. The position will provide a unique opportunity to play a foundational role in the development of FL103’s core platform.

Responsibilities

• Develop, maintain, and scale computational pipelines for proteomics, transcriptomics, and internal proprietary assay data.
• Integrate multi-modal biological datasets to identify disease-relevant molecular patterns, candidate biomarkers, and assay features.
• Design and apply statistical, machine learning, and bioinformatics methods to improve assay sensitivity, specificity, reproducibility, and biological interpretability.
• Partner with biologists, assay developers, and leadership to design experiments, define success criteria, analyze results, and validate key biological and computational hypotheses.
• Collaborate with software and data engineers to build internal tools, dashboards, and user interfaces that enable scientists to explore, interpret, and pressure-test FL103 data.
• Build literature- and knowledge-based contextualization workflows, including responsible use of LLMs, to connect internal findings with external scientific evidence and disease biology.
• Develop rigorous analytical frameworks for comparing candidate markers, assay conditions, biological cohorts, and disease states.
• Ensure analyses are reproducible, well-documented, and version-controlled, with clear standards for data provenance, code quality, and interpretation.
• Translate complex computational analyses into clear biological and strategic recommendations for cross-functional teams.
• Maintain deep scientific and technical expertise by staying current with advances in computational biology, liquid biopsy technologies, biomarker discovery, multi-omics analysis, and disease biology.
• Communicate results clearly through presentations, written reports, technical documentation, and cross-functional discussions with the FL103 team.

Qualifications

• PhD in computational biology, systems biology, bioinformatics, computer science or related fields with 2-4 years of industry experience
• Experienced in standard and advanced computational support of wet-lab experimental design
• Understanding of NGS approaches and demonstrated ability to collaborate with experimental biologists to conduct quality control, design experiments, and optimize protocols
• Experience analyzing -omics data (e.g., single-cell and bulk RNA-seq, mass spec) using a scientific programming language such as R or Python.
• Strong hands-on experience analyzing mass spectrometry-based proteomics data is required; direct experience with raw mass spec data processing, QC, normalization, and feature extraction is strongly preferred.
• Experience with both statistical inference and machine learning (e.g random forests, SVMs, neural networks, transformers, etc.).
• Able to manage multiple projects, working both independently and collaboratively within a dynamic team
• Excellent written and verbal communication skills to present results and scientific data to the internal team and/or collaborators
• Highly attentive to detail, flexible, and curious