Our client, a renowned research institution at the forefront of scientific discovery, is seeking a Principal Data Scientist with a deep specialization in Machine Learning for Genomics. This is a fully remote position, allowing you to contribute to groundbreaking research from anywhere. You will lead the development and implementation of advanced machine learning models to analyze complex genomic datasets, driving insights that accelerate biological understanding and therapeutic development. This role requires a unique blend of statistical rigor, computational expertise, and biological domain knowledge. You will work with massive datasets, tackle challenging scientific questions, and collaborate with world-class biologists, geneticists, and computational scientists.
Responsibilities:
Design, develop, and deploy sophisticated machine learning and statistical models for analyzing large-scale genomic and transcriptomic data. Collaborate closely with biologists and geneticists to understand research questions and translate them into data-driven analytical approaches. Identify and implement state-of-the-art algorithms for tasks such as variant calling, gene expression analysis, pathway analysis, and disease association studies. Develop pipelines for data preprocessing, feature engineering, and model evaluation using best practices. Explore and apply novel machine learning techniques, including deep learning, to uncover complex patterns in biological data. Author and contribute to scientific publications in leading peer-reviewed journals and present findings at international conferences. Mentor junior data scientists and bioinformaticians, fostering a culture of innovation and scientific excellence. Ensure the reproducibility and scalability of developed methods and code. Contribute to the strategic direction of data science initiatives within the organization. Work with large, diverse datasets, ensuring data integrity and security. Develop and maintain robust documentation for code, models, and analytical processes.
Qualifications:
Ph.D. in Bioinformatics, Computational Biology, Statistics, Computer Science, or a related quantitative field with a strong focus on machine learning and genomics. Minimum of 7 years of relevant research and development experience post-Ph.D., with a significant portion dedicated to genomic data analysis. Demonstrated expertise in applying machine learning techniques (e.g., supervised/unsupervised learning, deep learning, Bayesian methods) to biological problems. Proficiency in programming languages such as Python or R, and experience with relevant libraries (e.g., Scikit-learn, TensorFlow, PyTorch, Bioconductor). Hands-on experience with common genomic data types (e.g., WGS, WES, RNA-Seq, ChIP-Seq). Strong understanding of statistical principles and experimental design. Excellent analytical, problem-solving, and critical thinking skills. Superb communication and presentation skills, with the ability to explain complex concepts to both technical and non-technical audiences. Proven ability to work independently and collaboratively in a remote research environment.
This is a unique opportunity to advance your career and contribute to critical scientific breakthroughs remotely, based out of Ottawa, Ontario, CA .
