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Statistical analysis, predictive modelling, simulation, machine learning, and artificial intelligence. This high performing team works with clients to implement the full spectrum of data analytics and data science, from data querying and data wrangling to data visualization and dashboarding, to predictive analytics, machine learning, and artificial intelligence as well as robotic process automation (RPA.
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5+ years of experience as a Machine Learning engineer building production-level pre/post-processing data pipelines for ML/DL modelExperience in statistical analysis & visualization on datasets using SQL, Pandas or R.
$160,000 - $280,000 a yearExpandApply NowActive JobUpdated 3 days ago - UpvoteDownvoteShare Job
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Strong background in natural language processing, LLM, machine learning, and statistical modelling. - Statistical Modeling. Ph. D. or master's degree in biomedical NLP, Computer Science, Biomedical Informatics, Computational Linguistics, Mathematics, or other related fields.
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The Data Science team is hiring an experienced Machine Learning Engineer with a background building machine learning and statistical modeling frameworks from scratch. Design, prototype, implement, evaluate, optimize and monitor machine learning algorithms and related software systems to generate sports datasets and predictions with high accuracy and low latency.
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PhD plus 2 years (or Master plus 4 years) industry working experience in developing machine learning / statistical models to solve real world problems (e.g. classification, NLP, regression, panel models, time series, etc.
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Statistical/data science methods most commonly used in retail analyses including: forecasting, AB testing, hypothesis testing, machine learning and prediction algorithms; Access, query, aggregate, manipulate, consolidate, and summarize omni-channel customer and visitor data from multiple large-scale data sources using data science tools (SQL,R, Python, SAS, Microsoft Power BI, Azure, Adobe, Databricks.
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Knowledge in machine learning, artificial intelligence, statistical modeling, data visualization, and data analysis. Employ machine learning and statistical modeling to create and enhance data driven products.
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Basic to substantial experience in one or more of the following commercial/open-source data discovery/analysis platforms: RStudio, Spark, KNIME, RapidMiner, Alteryx, Dataiku, H2O, SAS Enterprise Miner (SAS EM) and/or SAS Visual Data Mining and Machine Learning, Microsoft AzureML, IBM Watson Studio or SPSS Modeler, Amazon SageMaker, Google Cloud ML, SAP Predictive Analytics.
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Has advanced understanding, and can apply knowledge of, complex mathematical techniques (but not limited to): statistical outlier methods, logistic regression, natural language processing (NLP), and various machine learning methods (e.g., random forest, XGBoost, etc.
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Proficiency in GraphPad Prism, Excel, PowerPoint, Zoom, and basic statistical data analysis required; bonus if experience with Benchling, Notion, Asana, or Python/R programming. Leverage NGS and machine learning techniques together with the sequencing and data science teams to improve the development of tailored AAV capsids.
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The LGR is in search of a motivated computational biologist/bioinformatician, who will use their skills as a seasoned, experienced bioinformatics programming professional with a broad understanding of computational algorithms and systems to apply computational and statistical methods to analyze and interpret data obtained from the next generation of functional genomics screens.
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Thorough knowledge of bioinformatics and machine learning methods used in image processing for high-throughput screens. Fluency in data science methods, including clustering, dimensional reduction and basic machine learning concepts.
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To qualify for the Principal Level: MS + 7 years or PhD + 3 years of experience, highly skilled in multiple technical areas including wafer processing, materials engineering with an emphasis on diamond and/or laser processing, along with advanced statistical analysis applied to high-volume manufacturing.
$70,000 - $300,000Full-timeExpandApply NowActive JobUpdated 3 days ago - UpvoteDownvoteShare Job
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Perform data exploration using a combination of statistical programming languages (including, but not limited R, Python, SQL, SAS) and deploy predictive analytics and machine learning techniques to improve risk prediction, improve reserve, trend and financial forecasting in a manner that is actuarially sound, and enable real-time results and operational efficiencies.
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Experience in statistical programming language (e.g., Python), applied machine learning techniques, and using OSS frameworks (e.g., TensorFlow, PyTorch). Experience in statistical programming language (e.g., Python), applied machine learning techniques, and using OSS frameworks (e.g., TensorFlow, PyTorch.
$114,000 - $168,000 a yearFull-timeExpandApply NowActive JobUpdated 2 months ago
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