Data Scientist- Genetics and Indications

About Alltrna

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.

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 

Alltrna is unlocking the power of programmable tRNA medicines to treat underserved patient populations. However, considering the broad applicability of our platform technologies, it isn’t self-evident which indications, disease models, and populations we should target. We use real world data to bridge that gap, and we are seeking a bioinformatics scientist with experience in large, real-world biological data sets to inform our pipeline and development strategy.

A successful candidate will have a Ph.D. in computational biology, bioinformatics, genomics or a 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.

 Core Responsibilities

1. Lead the strategy, acquisition and analytical integration of data streams from population genetic, genomic and epidemiological data sources
2. Generate internal databases, analytical pipelines, and scalable scoring tools in the fields of population genetics and epidemiology; developing data analytics focused on disease – gene mutation linkages
3. Create and maintain internal reference materials including genotype-phenotype connections, epidemiological landscape, preclinical and clinical studies, and competition analytics
4. Build interactive plots and GUIs to promote data accessibility for exploration by the entire team; craft innovative data visualizations and communicate complex results to interdisciplinary audiences
5. Continually cultivate scientific/technical expertise through critical review of the scientific literature, attending external conferences, and developing relationships with key opinion leaders.
6. Work effectively with a cross-functional biology, research informatics, and translational medicine functions

 Required Qualifications

1. Expert knowledge of computational biology tools and bioinformatics approaches for the analysis of population genetic, genomic and epidemiological data, including population genetics, genotype-phenotype connections, pharmacogenomics, and NGS studies at cell, tissue, and organismal levels
2. Proven track record of gene and transcript data sourcing and acquisition, including web data extraction; data clean up, analysis, and scoring
3. Proven track record in establishing robust computation biology workflows to generate high quality hypotheses for wet lab testing through integration of genomic, next generation sequencing (NGS) and other relevant data sets.
4. Deep understanding of statistical and algorithmic principles in data science with fluency in coding languages most relevant to biology & drug discovery (e.g., Python, R).
5. Strong data visualization and communication skills with the ability to clearly explain the implications of complex data sets and drive decision-making.
6. Ability to integrate multiple data sources and streams into unified data files
7. Generation of decision matrices and score cards for quantitative decision making

Preferred Qualifications

1. Experience with constructing internal sequence databases and scoring tools
2. Experience developing web-based or stand-alone GUI programs for data visualization (i.e., Dash, Flask, RShiny, D3).
3. Familiarity with natural language processing tools and applications
4. Proficient in coding best practi