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Proficiency in software development (SAS, R, Python, Matlab) and developing machine learning and predictive models. The highly-qualified Biostatistician will support clinical investigation with a focus on genetic studies, including design and analysis of clinical studies, estimate sample size and power, interpret results and write statistical sections of papers and grants collaboration with investigators affiliated with Harvard Medical School, grant and protocol development and review, and teaching and mentoring of junior investigators.
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3+ years of work and/or educational experience in machine learning or cloud computing, experience using statistics and machine learning to solve complex business problems, experience conducting statistical analysis with advanced statistical software, experience scripting languages, and packages, experience building and deploying predictive models, experience web scraping, and scalable data pipelines and experience with big data analysis tools and techniques.
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Develop new computational, machine learning, and statistical analysis methods for integrating and analyzing large-scale, scRNseq, CRISPR and genotypic data using current state of the art technologies.
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Support Defense and security clients as a Data Scientist performing data science, analytics, data wrangling, statistical analysis, data engineering, and machine learning. 5+ years of experience performing data science and analytics, such as data wrangling, data visualization, statistical analysis, predictive analysis, and machine learning.
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Solid understanding of statistical modeling, machine learning techniques, and their practical applications in quantitative research. Leverage your expertise in software engineering, data engineering, and machine learning to build scalable and robust systems that facilitate large-scale data analysis and experimentation.
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Familiarity with advanced statistical and machine learning techniques such as clustering, gradient boosting machines (GBM), support vector machines (SVM), neural networks (e.g., ANN, RNN, CNN), and other deep learning algorithms is a plus.
$185,800 - $323,400 a yearFull-timeExpandApply NowActive JobUpdated 8 days ago - UpvoteDownvoteShare Job
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Apply advanced statistical analysis, machine learning techniques, and deep learning algorithms to solve complex business problems. Develop and deploy machine learning models at scale, utilizing tools like Apache Spark, Databricks, and cloud platforms.
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We welcome scholars whose research enriches or expands the fields of theoretical statistics and/or probability and contribute to the advancement and expansion of the current research areas at MSU STT, including (but not limited to) actuarial science, Bayesian statistics, biostatistics, data science, stochastics processes, time series, spatial modeling, experimental design, high-dimensional statistical inference and machine learning.
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Proficiency in large scale data analysis and algorithm development for instance using machine learning, Markov chain, Bayesian, dynamic programming, graph analysis. Relevant PhD degree required in computational biology, computational neuroscience, applied mathematics, computer science, or related discipline required, with a strong bioengineering and/or molecular biology focus.
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Specialized experience for this position must include: Experience with data science and analytical methods from conducting machine learning, Natural Language Processing, and technical procedures such as, solution design, implementation, and deployment.
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Utilize Python machine learning, R statistical modeling, data mining, data warehousing techniques, and schema design skills. Act as a Subject Matter Expert for migrating on-prem applications to cloud-based PaaS focusing on data warehouse and big data solutions: Microsoft Azure HDInsight, Azure Databricks, Azure Synapse and Snowflake.
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Candidates must have deep knowledge of fundamentals of machine learning, data mining and statistical predictive modeling and experience applying these methods to real world problems.
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Maintain and develop new tools to investigate bid behavior and patterns using innovative technology such as machine learning and artificial intelligence, to enhance the visualization analysis capabilities for the MMA team when handling extensive data sets.
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In HBM DEG (High Bandwidth Memory, DRAM Engineering Group), we innovate and integrate end-to-end groundbreaking front-end and backend processes with groundbreaking design, debugging various tests, and qualification techniques to develop the lowest power per bit solutions to improve customer experience in the field of ML (Machine Learning) and AI (Artificial Intelligence.
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Performing quantitative and qualitative analysis and data quality assurance using statistical software and computer programs such as R and Python to extract information and build models machine learning and artificial intelligence.
ExpandApply NowActive JobUpdated 11 days ago
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