Scientist, Computational Biology in Cambridge

What if you could help build a life sciences company developing breakthrough technologies to transform how disease is detected, monitored, and treated?

Liquid biopsies have opened the possibility of understanding disease through minimally invasive biospecimens rather than relying solely on tissue biopsies and other invasive procedures.

Yet today's liquid biopsy technologies still capture only a fraction of the biology needed to detect disease early, monitor progression, and guide intervention across many high-burden conditions.

As a result, many diseases are still diagnosed only after symptoms emerge or after invasive testing, limiting the opportunity to intervene when treatments may have the greatest impact.

Position Summary

FL103 is seeking a highly motivated computational biologist to join our early-stage biotech company.

The successful candidate will be a driven scientist who is excited to develop and apply state-of-the art computational approaches to detect disease relevant cellular dynamics from minimally invasive biospecimens.

This position provides a unique opportunity to play a foundational role in building FL103's core platform and translating complex biological data into insights that can shape the future of disease detection, monitoring, and intervention.

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

• Ph D 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 spectrometry) 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.).
• Ability 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