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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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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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Skilled with models for DoD problems and is familiar with natural language processing, machine learning, predictive modeling, statistical analysis, and hypothesis testing. Experience in a data analytics, open-source tools, cloud computing, machine learning and data visualization.
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Demonstrated experience in applying advanced analytics techniques, including predictive modeling, machine learning, regression analysis and/or A/B Testing, to solve complex business problems.
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Requires three (3) years of experience in: (1) data analysis with Python and Python machine learning packages; (2) creating analytics tools to visualize KPIs in relation to equipment and process engineering; (3) MS Excel Macros and Pivot tables; (4) SQL, Tableau, and Spotfire; (5) knowledge of semiconductor process.
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This high performing team works with clients to implement the full spectrum of data analytics and data science, from data architecture and storage to data engineering and querying, to data visualization and dashboarding, to predictive analytics, machine learning, and artificial intelligence as well as robotic process automation (RPA.
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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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Experience leveraging complex data to drive business decisions, hands on experience in data science methodologies (predictive analytics, machine learning, patient level data triggers) using R, Pytong, Databricks and deep knowledge of Qlik, PowerBI, Tableau for visualization.
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O9 Solutions is looking for an experienced, talented, and motivated Lead Data Scientist to come join our global team in NA or EMEA. As part of this Data Science team, you will be involved in the end-to-end delivery of our supply chain planning products and building advanced forecasting models with Predictive Analytics, Machine Learning and AI with algorithms such as R, and Python.
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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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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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Integrating innovative approaches to individual and team work products such as machine learning, artificial intelligence, data mining, predictive modeling, neural networks and simulation to mitigate price/value leakage across the pursuit and delivery phases of an engagement.
$113,200 - $205,500 a yearFull-timeExpandApply NowActive JobUpdated 19 days ago - UpvoteDownvoteShare Job
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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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Statistical analysis experience, including experimental design, regression modeling, and machine learning using tools such as GCP, Adobe analytics, Python, R, Spark SQL and MLlib for custom analysis, in conjunction with SQL for data query and extraction techniques.
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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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machine predictive modeling analytics principal jobs Company: Inc in Raleigh, West Virginia
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