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We are on the forefront of CBRN defense and we are looking for talented Data Scientists that have applied experience in the fields of artificial intelligence, machine learning and/or natural language processing to join our team.
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As a data strategist, you're excited at the prospect of unlocking the secrets held by a data set, and you're fascinated by the possibilities presented by IoT, machine learning, and artificial intelligence.
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This candidate should be proficient in using BI tools such as Tableau, in addition to experience with one or more modern technologies such as relational databases, AWS, Snowflake, Python, Databricks, Git, and Machine Learning.
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Need deep knowledge of statistics, predictive modeling, machine learning, data mining, feature engineering. Qualifications Bachelor’s degreePhD desiredMaster’s at least required in statistics, data science, economics, psychology (research / experimental) or other heavy quant focused social or other science (biometrics.
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Position also requires two (2) years of experience in each of the following: Agile methodologies or SAFe Software Development Principles; Designing data models and solutions for analytical and reporting use cases; Machine learning, statistical analysis, and predictive modeling; Python, R, and SQL; Hadoop, Hive, MySQL, and NoSQL; Tableau and Power BI; and Software development lifecycle.
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Employs mathematics, statics, information science, artificial intelligence, machine learning, network science, probability modeling, data mining, data engineering, data warehousing, data compression, data protection, and / or other scientific techniques to correlate complex, technical findings into graphical, written, visual and verbal narrative products on trends of existing intelligence data to leverage other IC data sources.
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Experience in working with tools like SAS E-miner and use machine learning models to identify patterns and anomalies in transaction data indicative of fraudulent activity. Design and monitor key fraud metrics and KPIs to evaluate the effectiveness of fraud prevention strategies Identifying new sources of data (internal or vendor-provided) that can enrich our existing fraud detection processes, be added to our decision systems, and allow for new detection strategies to be developed.
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Develop innovative solutions that leverage machine learning, artificial intelligence, and cloud technologies to transform data handling and analytics. 12+ years of experience in data architecture, with a solid understanding of data modeling, ETL processes, and modern data frameworks (e.g., DataBricks, Snowflake, Azure.
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As a Data Scientist at Capital One, you'll be part of a team that's leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.
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Job title: Senior Machine Learning EngineerJob Description:As a Machine Learning Engineer (MLE), you'll be part of a lean software team dedicated to productionizing machine learning applications and systems at scale.
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Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment. Experience with one or more of the following: data processing automation, data quality, data warehousing, data governance, business intelligence, data visualization, data privacy.
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We are a cross-functional team aligning product, tech, design, machine learning, risk and other horizontal partners as we work to create a modern data ecosystem to support innovation and business across all corners of the company.
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Provide expert support in developing/refining Statistical and Machine Learning algorithms being deployed in data products/applications/services. This could include developing additional methodologies for delineating Current Range (e.g. using spatially explicit habitat modelling) and delineation of a Consultation Notification Area or buffers Provide support for making programmatic effort to recode ranges from Field Office based ranges to one range per species Requirements: Software development experience with statistical software and packages, including machine learning frameworks in R and Python.
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Deep knowledge of machine learning, data mining, statistical predictive modeling, and extensive experience applying these methods to real world problems. Extensive experience in Predictive Modeling and Machine Learning: Classification, Regression & Clustering.
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Job Description: Data Engineer Tech stack: Python, PySpark, AWS Responsibilities: Development and maintenance of data pipelines using Python, PySpark and various AWS services. Job Description: Data Engineer Tech stack: Python, PySpark, AWS Responsibilities: Development and maintenance of data pipelines using Python, PySpark and various AWS services.
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machine learning data jobs in Richmond, MT, Alabama
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