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Experience in NGS data analysis (expression profiling, mutations and structural variations, and differential peak analysis), data integration, and data mining. The primary purpose of the Associate Data Scientist position is to provide bioinformatics analysis of next generation sequencing (NGS) data for faculty across the institution, to process NGS data for Advanced Technology Genomics Core facility, to perform quality control (QC) analysis and to maintain QC metrics and sample records of NGS experiments on the servers, and to meet with faculty to review data analysis results.
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Performing data analysis using data mining and visualization techniques (Alteryx, Tableau, SAS, SQL, R, Python) Experience with risk data, reporting, and analysis; including systems.
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Should be skilled in data visualization and use of graphical applications, including Microsoft Office (Power BI) and Tableau; major data science languages, such as R and Python; managing and merging of disparate data sources, preferably through R, Python, or SQL; statistical analysis; and data mining algorithms.
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Strong experience in data research using SQL/SAS/R/Python for data analysis. Conduct data exploration, quantitative research and data mining to uncover money laundering patterns.
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What YOU Bring to the Table: Overview: 10-15+ years of professional experience in Marketing Analytics, Growth Marketing Analytics, Performance Marketing, Marketing Campaign Analytics, etc + Domain expertise in eCommerce software sales models Advanced Analytical Skills: Proficiency in statistical analysis, data mining, and predictive modeling.
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Manages clinical programming team specific to SDTM, metrics and dashboards, data cleaning, ad-hoc reporting, data review, data surveillance and data mining via strategic partnerships with preferred vendors.
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Relevant experience must be in designing/implementing machine learning, data science, advanced analytical algorithms, programming (skill in at least on high level language ( e.g. Python), statistical analysis ( e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management ( e.g. data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering.
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Knowledge, skills, and relevant experience in one or more of the following is required: - Designing and implementing machine learning - Data mining - Advanced analytical algorithms - Programming - Data science - Advanced statistical analysis - Artificial Intelligence - Computational science - Software engineering - Data engineering.
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Present data findings and recommendations to clients and stakeholders using your knowledge of databases, business intelligence tools, including Qlik and Tableau, scripting languages such as SQL, Python, and R, and the Microsoft Office Suite.
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Exceptional with Excel, manual data analysis and other data mining tools (Python, R, SQL) Strong analytic skills and a high degree of proficiency in data mining.
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5+ years of experience in data analysis and data mining and visualization tools such as Python, R, SQL, Tableau, or similar. Stay abreast of the latest trends, developments, and best practices in process excellence and mining, and incorporate them into the enterprise strategy and operations.
$131,500 - $303,200 a yearFull-timeExpandApply NowActive JobUpdated 4 days ago - UpvoteDownvoteShare Job
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Be skilled in data visualization and use of graphical applications, including Microsoft Office (Power BI) and Tableau; major data science languages, such as R and Python; managing and merging of disparate data sources, preferably through R, Python, or SQL; statistical analysis; and data mining algorithms.
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Proven computational experience with solid knowledge of informatics tools and visualization/data mining package. Proficiency in common database/programming/scripting languages (e.g., SQL, R, Python, Perl, Java, MATLAB, C.
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Expertise in advanced machine learning techniques, including deep learning, data mining, clustering, regression models, predictive modeling, optimization, and ranking. Proficiency in ML and Deep learning libraries in Python (MLlib, PyTorch, Keras, TensorFlow, etc.
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Experience in mathematics (statistics, linear algebra, differential calculus); data visualization (Tableau, Power BI, Qlikview); programming (SQL, Python, R, Java); data analysis (feature engineering, data wrangling, EDA) and machine learning (classification, regression, reinforcement learning, deep learning, clustering, dimensionality reduction.
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