(Senior) Data Scientist Product, Deep Genomics

• PhD in Computational Biology, Bioinformatics, Genomics, or a related field; or M.Sc. with 2+ years of relevant experience
• Demonstrated expertise in large-scale NGS data analysis, including amplicon sequencing, RNA-seq, and other molecular profiling methods
• Strong background in RNA biology, particularly RNA editing, splicing, or transcriptome analysis
• Strong programming skills (e.g., R, Python), with experience in cloud computing, data management, and bioinformatic tools
• Proven ability to manage computational aspects of cross-functional projects/programs and communicate complex computational concepts to both technical and non-technical audiences
• Strong analytical and problem-solving skills, with a focus on delivering practical, impactful solutions

Desirable

• We are seeking a highly motivated and collaborative (Senior) Computational Biologist to join our AI-guided RNA editing drug development team
• You will play a key role in the discovery, characterization, and optimization of RNA editing targets and therapies
• This position offers the opportunity to work at the intersection of computational biology, RNA therapeutics, and machine learning in a multidisciplinary and fast-paced environment
• The successful candidate will work closely with cross-functional partners— Biology, Machine Learning, and Engineering teams—to integrate computational approaches into all stages of research
• This is a full-time position in Cambridge reporting to a Principal Scientist on the Data Product team
• Design and implement computational pipelines for discovery-stage and preclinical programs. Build and maintain robust, scalable analysis pipelines and workflows to capture and analyze large-scale experimental datasets. In collaboration with the ML team, produce data packages that demonstrate the value of the AI platform in drug discovery
• Collaborate with users and the engineering team to optimize the computational discovery infrastructure enabling high-quality accelerated results
• Contribute to the design of experiments for model evaluation in partnership with the Target Identification and experimental teams
• Identify and integrate external datasets in collaboration with the engineering and machine learning teams
• Communicate findings and incorporate feedback with internal and external audiences through presentations and publications