About Alltrna

Alltrna is the world’s first tRNA platform company to decipher tRNA biology and pioneer tRNA therapeutics to treat thousands of diseases. Alltrna unlocks tRNA biology to correct disease. The company's platform incorporates AI/ML tools to learn the tRNA language and deliver diverse programmable molecules with broad therapeutic potential. Alltrna has an unprecedented opportunity to advance a single tRNA medicine to unify treatment across a wide range of diseases with the same underlying genetic mutation. Alltrna was founded in 2018 by Flagship Pioneering. For more info, visit www.alltrna.com.

About The Role

We are looking for a candidate who is creative, scientifically bold, independent, goal-oriented, has excellent communication skills, is motivated to learn new skills and works collaboratively in a fast-paced entrepreneurial environment.

The ideal computational biologist candidate has a Ph.D. in bioinformatics, computational biology, genomics, or related field, an additional 3+ years of relevant experience, and a proven track-record of scientific excellence as evidenced by a strong publication and/or patent record. The candidate has a strong background in human genetics and multi-omics data analysis, can use these data to support and generate testable hypotheses and models, has experience in both developing new methodologies and establishing bioinformatics pipelines, thrives in an interdisciplinary team environment and has demonstrated the ability to generate innovative hypotheses in unexplored biological fields. The candidate’s career goals, technical skills, and core competencies should be aligned with the description below.

Core Responsibilities

1. Apply your scientific knowledge, critical thinking skills and creativity to generate and test hypotheses for novel applications of our platform in emerging fields of RNA biology.
2. Identify, internalize, and mine public and in-house NGS and proteomic datasets by developing novel NGS analysis methods or applying existing pipelines in new contexts.
3. Integrate internal and external datasets to generate unique inferences for hypothesis generation and subsequent wet lab testing in an iterative fashion with a focus on target/disease selection, mechanism-of-action determination, and biomarker discovery.
4. Thrive on rapid prototyping, strategic triage of ideas, and quickly translating new approaches in the literature into broadly impactful company assets.
5. Build intuitive databases and interactive GUIs to promote data accessibility for exploration by the entire team.
6. Create innovative data visualizations and communicate complex results to interdisciplinary audiences
7. Be a champion for computational work documentation and reproducibility and continually cultivate scientific/technical expertise through critical review of the scientific literature, attending external conferences, and developing relationships with key opinion leaders.
8. Work effectively with a cross-functional biology, technology, and chemistry team to contribute to the vision, strategy, and tactics of the organization.

Qualifications and Skills

1. Proven track-record in identifying and integrating public domain and/or internally generated data sets (i.e. genetic, genomic, proteomic) to generate high-quality hypotheses and solve complex biological problems
2. Deep understanding of statistical, algorithmic and database principles in data science with fluency in coding languages most relevant to biology & drug discovery (e.g. Python, SQL, R).
3. Expertise in NGS data analysis (i.e. Illumina, Nanopore, single-cell) including execution of standard workflows and development of novel methods.
4. Clear understanding of cloud-based computing environments with a focus on AWS.
5. Demonstrated ability to approach complex problems with innovative solutions and generate actionable hypotheses
6. Familiarity with coding standards and best practices with a commitment to reproducible computational research.
7. Excellent data visualization and communication skills with the ability to explain complex concepts to a general scientific audience and drive decision-making

The preferred candidate will ideally also possess the following skills:

1. Established track record in the RNA biology field and expertise in RNA-centric NGS methods including Ribo-seq, RIP-seq, eCLIP and RNA chemical probing (e.g. DMS-MaP-seq, SHAPE-MaP).
2. Experience in discovery and targeted proteomics data analysis and visualization.
3. Application of modern algorithmic approaches including machine learning, deep learning and AI to solve complex biological, chemistry and/or drug discovery problems.
4. Experience developing web-based or stand-alone GUI programs for data visualization (i.e. Dash, Flask, RShiny, D3).