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The majority of these hands-on requests involve pulling data from Real World Data repositories such as Medical Claims, Electronic Health/Medical Records, Lab results, Census data, CDC Surveys (, NHANES) as well as internal systems of record to conduct a diverse range of analyses, primarily using tools like Snowflake SQL, R, Python, Tableau, others, while working with the team lead to deliver the results in a succinct and effective data story.
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Provide technical excellence to apply state-of-the-art machine learning / artificial intelligence utilizing technologies such as Spark, R, Hadoop, Scala, SQL to real world infrastructure business problems.
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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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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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The successful candidate will be skilled in the integration of tissue biomarker data with data from other sources (e.g. molecular genetic data, flow cytometry, proteomic data, clinical data, etc.
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Proficiency in data analytics tools such as Excel, SQL, SAS, Alteryx, MicroStrategy & Tableau. Broad knowledge of secondary pharmaceutical data sources such as Retail & Non-Retail data, Medical & Pharmacy Claims, EMR, Lab Test/Diagnostics, Chart Audits, Patient Hub & Specialty Pharmacy Data, 852/867 EDI and Health System/Payer – Plan hierarchies.
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Minimum of 5 years of experience in machine learning and AI, with a focus on large language models and generative AI.Versioning control tools, Git, Gitlab, CICD tools, Dockers and KubernetesKnowledge of data science, machine learning & deep learning models and expertise in statisticsExperience with machine learning frameworks such as TensorFlow, Keras, or PyTorch and other open sources ML frameworks.
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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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Skilled in data extraction and summarization from large datasets using SQL and basic statistical analysis using preferred languages (i.e., Python, SQL, R) required. Uncover "critical moments" along a customer's journey for marketing intervention using common sources of consumer and census data.
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The team's work is essential in supporting feature developments that use artificial intelligence and machine learning including Bloomberg Law's Docket Key, Points of Law, Brief Analyzer and Draft Analyzer, Bloomberg Tax's search capabilities and Bloomberg Government's State Bill Comparison.
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Here in MDM we work closely with cross-functional stakeholders to have efficient master data for our business. Master Data AnalystPrinceton, NJ, USHybridDo you enjoy working in a team environment as well as data management.
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Duties: Improve value to the business and patients through deepened market understanding, streamlined business engagement and practically applied, data driven analytics by drawing on experience with natural language processing using R or Python; regression and classification using Machine Learning; managing large analytical projects with matrix teams in global setting; development of analytical platform using business intelligence tools; and cloud-based technology.
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This position reports to Head of Biostatistics and Data Management (BDM) within Research and Development (R&D) Department at SPARC. Main responsibilities will include effective leadership, planning and oversight within the Clinical Data Management (CDM) function as well as facilitating collaboration across all R&D functions to ensure rigorous collection and analysis of data.
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Develops and executes strategies to identify and fulfil complex information needs company-wide using advanced techniques, methodologies, and technologies in text-mining, natural language processing, data mining, machine learning, and data visualization.
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Provide analytical and statistical support on HEOR research performed within claims data, EMR data, clinical trial data, and registry data. Work closely with HEOR principals to design HEOR studies to select the appropriate data (e.g., eCRF design, database design, registry data.
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