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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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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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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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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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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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Join the Anheuser-Busch NAZ Tech Data and Analytics Team as a Machine Learning Engineer. Leverage data science frameworks like TensorFlow, PyTorch, and Spark ML to build robust and scalable machine learning solutions.
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Julia, MATLAB, Scala, LISP, Prolog, and JavaScript for various AI/ML applications, including data analysis, high-performance computing, enterprise-level applications, and web-based MLExperience with AI and machine learning frameworks (, TensorFlow, PyTorch, Scikit-learn.
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Utilize Databricks for data processing, analytics, and machine learning tasks. The ideal candidate will have a strong background in SQL Server development, hands-on experience with Azure Pipeline, and proficiency in working with Databricks for data processing and analytics.
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We want Data Science/Machine learning/Data Analyst and Java Full stack candidates. entry-level software programmers, Java full-stack developers, Python/Java developers, Data analysts/ Data Scientists, and Machine Learning engineers.
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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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Backed by artificial intelligence and machine learning, Jerry simplifies and automates owning and maintaining a car while providing personalized services for all car owners' needs. Reporting to our VP, Data Science and Analytics and partnering closely with our marketing, product, and business leaders, you will run analyses, build attribution models, and design and run experiments to identify efficient acquisition channels and translate insights and learnings to inform our growth marketing investment decisions.
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Hands on experience in data engineering desired with extensive knowledge of SQL and PySpark, on Databricks Platform. Hands on experience designing, building, and optimizing data pipelines, architecture for large scale data warehousing projects.
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