Full Job DescriptionAt Thermo Fisher, our mission is to cure cancer with genomic data. It is a close-knit, collaborative, cross-functional setting where we contribute to the best of our abilities and talent to deliver on our commitments to science. The software services team is tasked with applying industry best practices to the design, development, and deployment world-class software products. We are looking to hire curious, motivated, self-driven individuals with experience in bioinformatics, molecular biology, data science in clinical and research settings. We want enthusiastic, passionate team players that can own the data strategy for our Machine Learning team. You will apply your expertise to do hands on annotation of datasets – both internal and obtained from customer sites in the field. You will organize this data and devise strategies to intelligently identify problematic data for algorithm development. We require a strong combination of domain and coding skills, and an interest in learning about the latest technologies we are trying to harness to solve these urgent problems in science.
Develop, improve, validate, and extend methods for the annotation of qPCR/CE/Sanger Sequencing data including both novel and existing methodsHands on annotation of customer and in-house datasetsDashboarding of available annotated dataWriting scripts to identify interesting data to aid machine learning.
You will remain up to date with the latest developments in genomics, qPCR analysis, Sanger Sequencing analysis, data mining and integration methods. You may engage with collaborators and you may attend and present at scientific meetings as appropriate.
Minimum Qualifications (must have)MS in Bioinformatics, Computational Biology or equivalent with experience (0-2 years) in Data Analysis (Sanger Sequencing, qPCR would be plus)Proficiency in Python is required. Experience in MATLAB, C++, R is a plus. · Understanding of algorithm development, statistics, data analysis is good to have. · Effective communication skills, especially for presenting scientific results, working collaboratively in a team, and documenting work.Ability to work in a multi-disciplinary and geographically distributed team environment.Ability to work in a fast paced environment.Strong drive for problem-solving and continuous improvement, with the willingness and courage to work through obstacles.Preferred Qualifications (nice to have)
Knowledge of AWS development
Background in machine learning theory and techniques.
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