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A highly motivated individual, the Senior Principal Scientist will focus on strategic development and implementation of novel data science and predictive analytics methodologies to support clinical drug development.
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Typical role includes working on a wide range of activities such as working with complex structured and unstructured datasets, developing/recommending novel machine learning tools, data visualizations, automation of analytics workflows, disease progression models, mechanistic and empirical PK/PD models, clinical trial simulations, literature meta-analysis using quantitative approaches and statistical modeling of historical and preclinical data.
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The Enterprise Business Intelligence Data Scientist is responsible for leading predictive analytics, statistical modelling, machine learning and data mining effort for implementing solutions and analysis of clinical care utilization and performance across the JPS Health Network.
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Knowledge of machine learning and AI methods and proficiency with scripting and executing data analytics algorithms and models with hands-on experience using a modeling and simulation software (e.g. Python, MATLAB, R, NONMEM, SAS, S-Plus, etc) is a must.
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This role demands a blend of expertise in data science and a deep understanding of the semiconductor industry, aiming to harness advanced data analytics, predictive modeling, and artificial intelligence to address complex business challenges.
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Senior Principal Scientist, Data Analytics and Pharmacometric Modeling Live. The Clinical Pharmacology, Modeling & Simulation Department at Amgen is seeking a Senior Principal Scientist to join the cross-functional global drug development teams at its South San Francisco, CA location.
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Senior Principal Scientist, Data Analytics and Pharmacometric Modeling page is loaded. Adapt data science algorithms (supervised and unsupervised learning, decision trees, neural networks, AI based image processing and feature extraction, Bayesian learning, etc) for modeling clinical trial data to support drug development.
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Perform exploratory data analysis and predictive modeling in pediatric biomedical research using machine learning, statistical, and mathematical analysis incorporating heterogeneous and complex data types under direct supervision.
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As Data Scientist Lead - Risk Data and Analytics, you will be responsible for developing AI models using machine learning, deep learning, and natural language processing, particularly transformer models and Generative AI. Your expertise will be instrumental in developing and implementing predictive modeling solutions for the risk and compliance organization, enabling them to proactively identify and mitigate risks.
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Scientific background in life sciences or engineering and demonstrated expertise in one or more of the following areas: data analytics, data visualization, statistics, machine learning, predictive modeling, decision analysis under uncertainty, artificial intelligence, Python and/or R, app development, and Tableau dashboard development.
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Bachelor's degree in Statistics, Computer Science, Mathematics, Machine Learning, Econometrics, Physics, Biostatistics or related Quantitative disciplines and 5 or more years experience in predictive analytics or equivalent.
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6 years proven experience in training and validating machine learning, statistical, physical, and other advanced analytics models. 8 years of experience in a predictive analytics or data analysis OR Advanced Degree (e.g., Master’s, PhD) in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative field and 6 years of experience in predictive analytics or data analysis.
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Experience in core data science and predictive analytics methods: Statistics (t-tests, Poisson process), Segmentation and clustering techniques, predictive modeling: e.g., regression, classification, Time Series analysis: e.g., ARIMA, Traditional machine learning methods: e.g., Random Forest, ensemble model techniques, Optimization: e.g. linear programming.
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You have 2-5 years of successful technical experience in the domain of predictive analytics (e.g. data science, machine learning, data mining, and statistics related work); preferably in the Oil & Gas industry.
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Develop machine learning models for forecasting, regression, classification, clustering, and advanced statistical analysis. Serves as a mentor to data scientists in modeling, analytics, computer science, discernment, and other interpersonal skills.
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machine learning predictive modeling analytics principal scientist jobs Company: Inc in Raleigh, West Virginia
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