Scientist, Computational Biology

About the role

Join our innovative team at FL103, where you will play a key role in developing groundbreaking technologies aimed at transforming disease detection, monitoring, and treatment. As a computational biologist, you will leverage advanced computational methods to analyze complex biological data derived from minimally invasive biospecimens. Your work will be instrumental in shaping our core platform, enabling us to identify novel biomarkers and improve assay performance. This position offers an exciting opportunity to collaborate with cross-functional teams, including biologists, assay developers, and engineers, to drive scientific discovery and technological innovation in the field of liquid biopsy and biomarker research.

Key facts

What you'll do

• Develop, implement, and maintain computational pipelines for processing and analyzing diverse biological datasets, including proteomics, transcriptomics, and proprietary assay data.
• Integrate multiple types of biological data to identify molecular patterns, potential biomarkers, and assay characteristics that are relevant to disease states and biological processes.
• Apply statistical, machine learning, and bioinformatics techniques to improve assay sensitivity, specificity, reproducibility, and robustness.
• Collaborate closely with biologists, assay developers, and project management teams to design experiments, define success criteria, and interpret data outcomes.
• Assist in designing experiments by providing computational support, including quality control, data analysis, and protocol optimization, to ensure high-quality biological data collection.
• Work with software and data engineers to develop internal tools, dashboards, and user interfaces that facilitate data exploration, visualization, and interpretation for scientists and stakeholders.
• Establish workflows that incorporate literature review and knowledge-based contextualization, including responsible use of large language models (LLMs), to connect internal findings with external scientific research and databases.
• Create analytical frameworks for comparing candidate markers, assay conditions, biological cohorts, and disease states to prioritize promising approaches.
• Ensure all analyses are reproducible, well-documented, and version-controlled, following best practices for data provenance, code quality, and documentation standards.
• Translate complex computational results into clear, actionable biological insights and strategic recommendations for cross-functional teams and leadership.
• Stay current with advancements in computational biology, liquid biopsy technologies, biomarker discovery, multi-omics analysis, and disease biology to continually enhance analytical approaches.
• Present findings effectively through presentations, written reports, and discussions with the FL103 team, ensuring clear communication of complex data and insights.
• Contribute to the ongoing development of internal standards, best practices, and methodologies for computational analysis within the organization.
• Support the evaluation of new computational tools, algorithms, and approaches to ensure the team remains at the forefront of technological innovation.
• Participate in scientific discussions, seminars, and conferences to share knowledge and learn about emerging trends in the field.

Requirements

• PhD in computational biology, systems biology, bioinformatics, computer science, or a related discipline, with 2-4 years of relevant industry experience.
• Proven experience in providing computational support for wet-lab experimental design, including both standard and advanced techniques.
• Familiarity with next-generation sequencing (NGS) methods and the ability to collaborate effectively with experimental biologists for quality control, experiment design, and protocol optimization.
• Hands-on experience analyzing -omics data such as single-cell RNA-seq, bulk RNA-seq, and mass spectrometry data using programming languages like R and Python.
• Strong background in statistical analysis, machine learning, and data modeling techniques relevant to biological data.
• Ability to interpret complex datasets and translate findings into biological insights and hypotheses.
• Excellent communication skills, capable of conveying complex analyses to diverse audiences, including non-technical stakeholders.
• Experience working in a multidisciplinary team environment, demonstrating collaboration and adaptability.
• Knowledge of data management, version control, and reproducibility best practices.
• Ability to work independently, prioritize tasks, and meet deadlines in a fast-paced research environment.

Nice to have

• Experience with liquid biopsy technologies and biomarker discovery projects.
• Familiarity with software development practices, including code review, testing, and deployment.
• Knowledge of ITAR regulations and handling of sensitive biological data.
• Experience with cloud computing platforms and high-performance computing environments.
• Understanding of assay development processes and clinical validation workflows.

Skills & tools

• Proficiency in programming languages such as R and Python for data analysis and modeling.
• Familiarity with bioinformatics software and tools for sequencing data analysis, such as Bioconductor, GATK, or similar platforms.
• Experience with data visualization tools and dashboards for data exploration and presentation.
• Knowledge of version control systems like Git for code management.
• Ability to document workflows, analyses, and code clearly and thoroughly.

Practical notes

• This position is open to candidates who require visa sponsorship.
• The role is based on-site in Cambridge, MA, and requires physical presence at the office.
• Benefits include health insurance, retirement plans, and opportunities for professional development.
• The application process will remain open until the position is filled.
• Candidates are encouraged to demonstrate their experience with collaborative projects and their ability to adapt to a dynamic research environment.