Senior Computational Biologist, Data Science

Company Summary

FL89, Inc. is a privately held early-stage company that is leveraging advanced biological and computational tools to develop breakthroughs in our understanding of secreted protein function and regulation in human physiology. More specifically, FL89 is pairing high throughput -omics technology with AI/ML based protein structure prediction to define and discover novel therapeutic protein biology.

Flagship Pioneering has conceived of and created companies such as Moderna Therapeutics (NASDAQ: MRNA), Editas Medicine (NASDAQ: EDIT), Omega Therapeutics (NASDAQ: OMGA), Seres Therapeutics (NASDAQ: MCRB), and Indigo Agriculture. Since its launch in 2000, Flagship has applied its unique hypothesis-driven innovation process to originate and foster more than 100 scientific ventures. In 2021, Flagship Pioneering was ranked 12th globally on Fortune’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies.

Position Summary:

We are seeking a talented and driven ML scientist with a strong focus on genomics and an interest in proteomics to join our dynamic team. The ideal candidate will have a solid background in computational genomics and a passion for leveraging AI/ML techniques to advance research, and thrives in a fast-paced and collaborative startup environment. The position will provide a unique opportunity to play a critical role in the advancement of FL89’s computational discovery and preclinical platforms, through the development of disruptive approaches for biotherapeutic drug discovery.

Key Responsibilities:

• Apply AI/ML in genomics research: Utilize advanced computational models and machine learning algorithms to analyze genomic data, to gain valuable insights into the underlying biology.
• Collaborate with interdisciplinary teams: Work closely with biologists and data scientists to integrate genomic data with other omics data, such as proteomics and transcriptomics, to unravel complex biological mechanisms.
• Database Modeling and analysis: Utilize graph databases and graph-based ML algorithms for biomedical data analysis, integrating diverse datasets, and developing predictive models to explore complex biological relationships and networks for advancements in genomics research and personalized medicine.
• Data Engineering: Utilize data engineering techniques to efficiently process, transform, and integrate large-scale genomics and biomedical datasets, ensuring data quality and accessibility for model development and analysis using graph databases and algorithms.
• Machine learning model development: Design and deploy machine learning models for genomics data analysis, including gene expression prediction, and functional annotation.
• Contribute to scientific publications and conferences: Share research findings through publications in reputable journals and present results at conferences and workshops to contribute to the genomics research community.
• Ensure data integrity and reproducibility: Implement best practices in data management and software engineering to ensure data integrity, reproducibility, and transparency in genomic analyses.
• Embrace a fast-paced environment: Thrive in a collaborative and fast-paced research environment, adapt quickly to evolving project needs, and contribute to a positive team atmosphere.

Minimum Qualifications:

• PhD or MS in computational biology, bioinformatics, genomics, or a related field with a strong focus on genomics or proteomics research.
• 3+ years of hands-on experience applying AI/ML techniques in genomics or proteomics data analysis.
• Strong understanding of genomics data analysis methods, differential expression analysis, and functional annotation.
• Solid knowledge of statistical methods and data analysis techniques in the context of genomics.
• Experience in developing machine learning platforms using modern ML frameworks for deep learning (e.g. PyTorch, tensorflow, keras, MXNet) & deploying in services such as Amazon Sagemaker.
• Proficiency in programming languages and statistical analysis packages in Python or R.
• Knowledge of best practices in software engineering for reproducibility and data management skills
• Experience with genomics databases, tools, and resources.
• Solid knowledge of statistical methods and data analysis techniques in the context of genomics.
• Ability to work collaboratively in a multidisciplinary team and contribute to a positive and inclusive work environment.

Preferred Qualifications:

• Proficiency with bioinformatics tools in the protein space (e.g. AlphaFold, ESM,USalign,Rosetta, etc.)