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S&P is a leader in risk management solutions leveraging automation and AI/ML. This role is a unique opportunity for hands-on entry-level ML scientists and NLP/Gen AI/ LLM scientists to grow into the next step in their career journey and apply her or his technical expertise in NLP, deep learning, GenAI, and LLMs to drive business value for multiple stakeholders while conducting cutting-edge applied research around LLMs, Gen AI, and related areas.
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Under this program, we will make you stand out from the rest of the jobseekers by offering Skill enhancement training on the technologies in-demand including time management skills, study skills, test-taking skills, and learning strategies after which we can assist candidates in getting jobs as software programmers / Java Programmers / Data scientists / Machine learning engineers / Data analysts.
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We are looking for a Senior Machine Learning Engineer to dive into a fast-growing startup developing models that improve data processing pipeline and our client deliver new features to the market with unmatched speed and accuracy.
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Summary:We are seeking a motivated and skilled Data Scientist to join our team at DistroKid. The successful candidate will have a significant opportunity to design, develop and deploy Machine Learning models that empower our internal teams and make a real difference to our global community of artists.
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Experience with data ingestion technologies such as Azure Data Factory, SSIS, Pentaho, AlteryxExperience with visualization tools such as Tableau, Power BIExperience with machine learning tools such as mlFlow, Databricks AI/ML, Azure ML, AWS sage maker, etc.
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What You'll DoProcess, cleanse, and verify the integrity of data used for downstream analytics productsApply advanced machine learning, graph analytics, data mining, statistical methods, algorithms, and time series models to vast amounts of user data to derive insights based on the knowledge graph (ontology)Participate in strategic planning and be responsible for the team roadmap and its execution.
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Azure Platform Experience: The candidate should have hands-on experience with various Azure services including but not limited to Azure Data Factory, Databricks, Data Lake, and Power BI. They should be able to design, build, and maintain ETL pipelines, manage data lakes, and create insightful reports and visualizations.
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The candidate should have hands-on experience with various Azure services including but not limited to Azure Data Factory, Databricks, Data Lake, and Power BI. Databricks Machine Learning Certificate Preferable.
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4- 8 years of experience as machine learning engineer/data scientist. Aggregate huge amounts of data and information from large numbers of sources to discover patterns and features necessary to build machine learning models for prediction and forecasting.
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Various disciplines are involved in Enterprise Data & Analytics, including data source identification and analysis, data engineering, data visualization, and data science/machine learning.
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Expertise with CI/CD processes and contributes to a sustainable and robust codebaseExperience with batch jobs/data pipelines and is comfortable deploying through these systems (i.e. Airflow, Luigi, Composer)Build and interact with production systems for serving machine learning predictions.
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3-5 years of experience managing Paid Search (PPC) campaigns within Google Ads, including Performance Max, YouTube, and Display. As the Paid Search Specialist, you'll be responsible for managing and optimizing paid search and Google Ads campaigns for our Broadway and theatrical clients.
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Develop and optimize machine learning algorithms and models using Python, with a focus on performance and scalability, working very closely with the Data Scientists. Join the Anheuser-Busch NAZ Tech Data and Analytics Team as a Machine Learning Engineer.
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Experience leading teams in data analytics and data visualization, including selecting and applying the appropriate analytical techniques for statistical analysis, predictive modeling, simulation, and machine learning.
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Experience with digital data from ad serving platforms (e.g. Google Campaign Manager, Other adservers), campaign planning tools (e.g. Prisma, Lumina, MediaOcean), website analytics software (e.g. Adobe Analytics, Google Analytics), paid search engine marketing data sources (e.g. Adwords, Marin.
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