Computational Biologist

Scispot is a fully configurable workflow automation platform for fast-growing life science companies. Our customers use Scispot to design and automate their workflows at all stages of their R&D, from planning, lab execution to reporting collaboratively. Scispot is a trailblazing company developing the world's best and first data infrastructure for lifescience companies. We work at the intersection of biotech, AI, and ML to enable innovation and advancement in the life sciences industry. We are seeking a Computational Biologist with extensive experience in AI and Machine Learning, and a background in biotech. The successful candidate will work with our data scientists and bioinformatics teams to design and implement ETL pipelines and computational frameworks, integrate and analyze genomic and transcriptomic datasets, and assist in the interpretation of gene expression data. Run biotech R&D pipelines as a data scientist or data engineer Build ETL / ELT pipelines Work with open-source Apache products such as Airflow, Nifi Establish computational frameworks for integrating, accessing, and analyzing genomic and transcriptomic datasets Develop analysis workflows for high-throughput DNA, and RNA sequencing data Integrate new pipelines with other components of Scispot’s tech stack Assist in the analysis and interpretation of gene expression data Work with scientists to leverage Bioinformatics datasets and assist in efforts of target assessment Collaborate with wet-lab scientists, data scientists, and bioinformaticians to design experiments, analyze data, and interpret the results Recommend cleansing and transformation rules required to make the datasets ready for AI/ML Experience in data governance modeling Experience working with HTS Omics sequencing data (Eg: RNAseq, scRNAseq, ISLAND, WES) Run biotech R&D pipelines as a data scientist or data engineer Build ETL / ELT pipelines Work with open-source Apache products such as Airflow, Nifi Establish computational frameworks for integrating, accessing, and analyzing genomic and transcriptomic datasets Develop analysis workflows for high-throughput DNA, and RNA sequencing data Integrate new pipelines with other components of Scispot’s tech stack Assist in the analysis and interpretation of gene expression data Work with scientists to leverage Bioinformatics datasets and assist in efforts of target assessment Collaborate with wet-lab scientists, data scientists, and bioinformaticians to design experiments, analyze data, and interpret the results Recommend cleansing and transformation rules required to make the datasets ready for AI/ML Experience in data governance modeling Experience working with HTS Omics sequencing data (Eg: RNAseq, scRNAseq, ISLAND, WES) Currently pursuing PhD or MSc in Bioinformatics, Computational Biology, or a related field Demonstrated expertise in R, Python, and building complex code in a collaborative environment Experience supporting and working with cross-functional teams in a dynamic environment Experience working with high-throughput sequencing data (RNAseq, scRNAseq, WES) Proficiency in biological database management and the use of bioinformatics tools and software Extensive knowledge in applying machine learning and statistical modeling to biological datasets Proficiency in processing and analyzing high dimensional biological data Familiarity with public biological data sources and repositories Experience with biological data standards, ontologies, and metadata Familiarity with cloud-based data storage and analysis solutions is preferred Strong analytical skills related to working with large diverse datasets A demonstrated ability to find creative and functional solutions to complicated problems Excellent documentation skills with impeccable attention to detail Exceptional communication skills and the ability to express complex ideas in understandable ways Strong critical thinking skills and the ability to handle ambiguity in data analysis Proven track record of publishing relevant work in high-impact journals is preferred If you are passionate about contributing to groundbreaking work in the life sciences and enjoy working in a fast-paced, innovative environment, we would love to hear from you. Currently pursuing PhD or MSc in Bioinformatics, Computational Biology, or a related field Demonstrated expertise in R, Python, and building complex code in a collaborative environment Experience supporting and working with cross-functional teams in a dynamic environment Experience working with high-throughput sequencing data (RNAseq, scRNAseq, WES) Proficiency in biological database management and the use of bioinformatics tools and software Extensive knowledge in applying machine learning and statistical modeling to biological datasets Proficiency in processing and analyzing high dimensional biological data Familiarity with public biological data sources and repositories Experience with biological data standards, ontologies, and metadata Familiarity with cloud-based data storage and analysis solutions is preferred Strong analytical skills related to working with large diverse datasets A demonstrated ability to find creative and functional solutions to complicated problems Excellent documentation skills with impeccable attention to detail Exceptional communication skills and the ability to express complex ideas in understandable ways Strong critical thinking skills and the ability to handle ambiguity in data analysis Proven track record of publishing relevant work in high-impact journals is preferred If you are passionate about contributing to groundbreaking work in the life sciences and enjoy working in a fast-paced, innovative environment, we would love to hear from you. We use AWS as our primary hosting service. Our tech stack comprises a Next.js frontend web app. On the backend, we use a NoSQL database (AWS DynamoDB) which is fronted by a GraphQL database abstraction API layer. We also use AWS Lambda as the middleware for our platform. To manage our user authentication, authorization and signups, we use AWS Cognito in combination with AWS IAM.