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Git); SQL Server, Javascript, Microsoft Office ApplicationsBachelor’s degree in museum studies, Information science, computer science or related field with proven interest in cultural heritage data management.
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Enrich oncology biomarker projects through interaction with the oncology translational research group and external leaders to ensure common scientific standards for biomarker programs, maximize efficiency and minimize redundancy by enabling data sharing between teams.
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If you are a passionate and driven Business Intelligence Developer with a strong background in ETL, SQL, Data Modeling, and Visualization, we encourage you to apply by submitting your resume to bk@ Please add me on LinkedIn.
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Co-own with relevant stakeholders the site HSE continuous improvement plan considering Firmenich HSE global Strategy, HSE global management Principles & Codes, Data, new business requirements, new/updated regulations and employees’ input and by providing data analysis, trending, and recommendations and providing technical and administrative direction on HSE matters at the site.
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Our Data team supports the Bloomberg Industry Group (INDG) business, which offers outstanding tools, search, and analytics for products in the areas of Law, Government and Tax. Bloomberg is changing the legal industry by delivering the most sophisticated research platform on the market with a focus on automation, analytics and real-time answers.
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ML, NLP, LLM Model Evaluation: Work closely with the other data science team members to develop, validate, and maintain robust evaluation solutions and tools to evaluate model performance, accuracy, consistency, reliability, during development, UAT. Implement model optimizations to improve system efficiency.
$150,000 a yearFull-timeExpandApply NowActive JobUpdated 22 days ago - UpvoteDownvoteShare Job
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Understanding of the different data and information systems such as ELN, LIMS, ERP systems, and LCM or PLM systems. We partner collaboratively with our clients to develop products that create value and deliver company results across critical areas of their business including portfolio strategy, customer insights, research and development, operational and technology transformation, marketing strategy and many more.
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How you’ll make an impact: As a Staff ML Ops Engineer at SiriusXM, you will be a key player in our Data Platform Team. Your role will be pivotal in deploying, managing, and optimizing machine learning (ML) models, leveraging advanced tools like Databricks and MLFlow.
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Development and implementation of comprehensive site investigations by participating in scope of work development, field work, and data analysis to characterize contaminants in sediments, soil, air, surface water, and groundwater.
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Minimum 8 years of experience in building ETL/ELT pipelines in AWS/GCP using various data engineering tools. 3 years of experience in data pipeline and workflow orchestration tools like Apache Airflow.
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Areas of responsibility include overseeing all functions that relate to data entry, gift processing, records management, and reporting. Manage gift processing and data entry staff of 1.
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Expertise, in-depth hands-on experience is expected in genomics platforms (NGS, qPCR, variant analysis platforms) for assay development, qualification/validation, troubleshooting, and reviewing run QC data.
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Familiarity and expertise with the following technologies is preferred: programming languages such as R or Python with a specific focus on data analysis, MS Access, MS Excel and ESRI ArcGIS. Experience with MS SQL Server (SSMS) and/or SQL script writing is an asset.
$100,300 a yearFull-timeExpandApply NowActive JobUpdated 12 days ago - UpvoteDownvoteShare Job
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Demonstrate competency with laboratory work and use of analytical instrumentation Interpret XPS analytical data and apply understanding of the data to solve specific problems Knowledge/experience in other surface analysis techniques is a strong plus, e.g. XRF (X-Ray Fluorescence), SEM (Scanning Electron Microscopy), RBS (Rutherford Backscattering Spectrometry.
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Deep understanding of payer data including claims, provider network management, care management, quality, and member experience. Expertise with cloud solutions such as cloud adoption and migration, cloud-based data lakes, modern data warehouses, and data modeling.
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data job Title: marketing in Princeton, NJ
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