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Data science, machine learning, optimization models, Master's degree in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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There are various disciplines involved in Enterprise Data & Analytics, including: data source identification and analysis, data engineering, data visualization & data science/machine learning.
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Serve as a strategic leader as part of our data science & machine learning team, with responsibility for Xometry's pricing strategy. Build collaborative relationships with key stakeholders; set priorities aligned to business goals; communicate analysis, strategies, timelines, and work of the data science & machine learning team, and gain buy-in from executive leadership.
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The Clinical Data Science team at Eisai, Inc., is expanding its capabilities by acquiring in-house talent related to bioinformatics, machine learning, and big data analytics for the Oncology and Neurology clinical programs.
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Demonstrated expertise in statistical theory and a wide range of statistical methods and machine learning techniques for high-dimensional multi-omics data. Develop and publish relevant bioinformatics methodologies, pipelines, and algorithms, and stay abreast of the latest advancements in bioinformatics, machine learning, and oncology research.
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Derive novel prognostic and predictive signatures using statistical and machine learning methods to guide patient stratification and biomarker selection in clinical trials. The base salary range for the Associate Director, Clinical Data Science - CEG is from :159,000-208,600.
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Perform integrative analyses of multi-omics data from clinical trials and research cohorts, including genomics, transcriptomics, proteomics, and imaging, to identify biomarkers related to treatment mechanism and response, and potential therapeutic targets.
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Establish, develop, and implement optimal bioinformatics pipelines for the analysis of next-generation sequencing and other -omics data. Collaborate with translational scientists and clinicians to design and execute data analysis strategies that address key clinical oncology research questions.
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Extensive experience and expertise, demonstrated via a strong publication record and prior work experience, in the analysis of cancer genomics data, including whole-genome sequencing, exome sequencing, RNseq, and epigenomic data.
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PhD, MS + 2 years, BS + 4 years of work experience in Computer Science, Machine Learning, Mathematics, Statistics, Operational Research or related quantitative fields. Stay informed and integrate latest statistical modelling, machine learning, deep learning, recommender system and/or artificial intelligence trends to improve and enhance modelling stack and improve business metrics and customer experience.
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The Scientific Software Engineering & Data Management Group in the X-Ray Science Division (XSD) at the Advanced Photon Source (APS) ( invites applicants for a postdoctoral position to develop and apply advanced analysis methods, including artificial intelligence and machine learning algorithms and approaches, for x-ray science and instruments.
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Relevant skills include statistical analysis and open-ended data mining (social network analysis will be particularly helpful); applied machine learning including Natural Language Processing; quantitative content analysis; qualitative discourse analysis; survey research; and archival research.
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Much of our work contributes to innovative research in the fields of sensor science, signal processing, data fusion, artificial intelligence (AI), machine learning (ML), and augmented reality (AR.
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Assist in Problem Solving: Troubleshoot complex issues related to machine learning model development and data pipelines and develop innovative solutions. ML, NLP, LLM Model Evaluation: Work closely with the other data science team members to develop, validate, and maintain robust evaluation solutions and tools to evaluate model performance, accuracy, consistency, reliability, during development, UAT. Implement model optimizations to improve system efficiency.
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You will be part of a team which develops machine learning/deep learning models for building maps, learning geocodes, organizing addresses, estimating transit times, and understanding transporter activities.
$136,000 - $222,200 a yearFull-timeExpandApply NowActive JobUpdated 3 months ago
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