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With minimal guidance, write, document, and deploy custom code in a variety of environments (Python, SAS, R, etc.) In supporting rule analysis, Teammate should be able to Independently perform sophisticated data analytics that fit to the fraud problem being observed (ranging from classical econometrics to machine learning, neural networks, and natural language processing) in a variety of environments using structured and unstructured data.
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Training in software, such as Stata, R, Python, SQL, Julia or Matlab. Stata, R, Python, SQL, Julia or Matlab). What opportunities are there for career development and continued learning.
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Experience with AirFlow, Beam, Spark, BigQuery and other cloud based and distributed technologies to scale machine learning models and workflows will be an asset. Thorough knowledge of relevant programming languages (primarily Python, SQL), object-oriented programming principles, Big Data tools and Data Mining/Warehousing Programs (preferably Google Cloud BigQuery.
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Currently, We are looking for entry-level software programmers, Java Full stack developers, Python/Java developers, Data analysts/ Data Scientists, Machine Learning engineers for full time positions with clients.
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Desired Background PhD in Computer Science or related field 4-8 years of professional experience with relevant experience building and designing distributed data systems Knowledge of statistics, data science, AI/machine learning, big data management Strong proficiency in Python and an additional language (e.g., C.
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Responsible for the administration, configuration, and optimization of the Databricks platform to enable data analytics, machine learning, and data engineering activities within the organization.
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Production Execution (Plant manufacturing processes, integration with MES via MII and other Shop floor systems, Recipe Management, Global Label Printing solutions) Quality Management (In Process, Inbound and Outbound Inspections, Integration with Coca-Cola Laboratories and R&D) Plant Maintenance (Corrective Maintenance, Preventive Maintenance, MRO management.
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You will have indepth knowledge and experience in; cloud data lake house; Databricks (preferred) or Snowflake; cloud-native solutions knowledge; SQL or Python; modern DevOps approach; have written data code AND also the CI/CD pipelines.
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Apply familiarity and skill with additional programming languages (e.g., Python, R), statistics, machine learning tools, data structure, data modeling, cloud computing, MS Azure, and Databricks to expand and enhance surveillance data value.
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Experience with multiple software languages and analytics tools (e.g., Spark, R, Python, SQL, Tableau, PowerBI, Microsoft Azure ML/AI Studio or other combinations) Create data science assets and automations using a combination of tools and languages including SQL, Python, R, Alteryx, Tableau, Microsoft Azure (Fabric, ML Studio, ADF, Synapse, PowerBI, etc.
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Advanced working knowledge of all aspects of the R and SAS programming language used in clinical trials programming. Solid knowledge of new advanced statistical methods using SAS or R required.
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Java, Python, or JavaScript " Experience with Windows, Lunix / Unix, scripting (PowerShell, or Perl), Active Directory, LDAP, SQL, and web services. Professional Skills " 6+ years of experience " Identity Management (SailPoint Administration) " Extensive hands-on knowledge of IAM best practices, procedures, and software solutions such as CyberArk, Okta, Ping Identity, and Directory Services.
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Relevant areas of experience might include deep learning techniques, systems software, high performance computation, numerical algorithms, data science, cheminformatics, medicinal chemistry, structural biology, molecular physics, and/or quantum chemistry, but specific knowledge of any of these areas is less critical than intellectual curiosity, versatility, and a track record of achievement and innovation in the field of machine learning.
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The Sr. Lead, Internal Audit Data Scientist will use advanced analytics, machine learning, and data visualization techniques to address some of Rivian’s most significant and most complex risks.
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Create new reporting and dashboards, data infrastructure, and queries to track key marketing metrics across multiple data sources leveraging SQL, R/Python, Looker, Tableau, and other visualization tools; monitor for changes in trends, share insights and make recommendations.
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