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AI Model Optimization: Work closely with data scientists and machine learning engineers to optimize NLP models, dialog management, and virtual agents. Expertise in Google Cloud Technologies: Hands-on experience with Google Cloud Platform (GCP), including Dialogflow, BigQuery, Google Kubernetes Engine (GKE), and AI/ML services.
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In our Data science track we prepare you to get job as one of the following: Python developer, a data analyst, data visualization developer, a statistician, a machine learning engineer or a data scientist.
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Lead and participate in solutioning sessions, working hand-in-hand with businesses/clients, translating business objectives into actionable machine learning/data science use casesWhat You'll Bring.
$186,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Proficiency in deploying scalable machine learning solutions in production environments, leveraging cloud platforms (AWS Cloud) and technologies like Docker and Kubernetes for containerization and orchestration.
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Serve as a trusted advisor to decision-makers, offering consultative insights on how the Nvidia DGX AI platform and machine learning can be applied to their biological data challenges.
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Familiarity with cloud-based computing solutions (AWS, Google Cloud) for scalable data analysis. Platforms: Nvidia AI, Nvidia DGX, AWS, Google Cloud for deploying scalable solutions.
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The Data Science team leads the strategy, development and integration of Machine Learning and Artificial Intelligence. Build production ready prototypes for, and iteratively develop, end-to-end data science pipelines including custom algorithms, statistical models, machine learning and artificial intelligence functions to meet end user needs.
$140,000 - $150,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Data Science & Machine Learning Frameworks. AWS SageMaker (for end-to-end machine learning development, training and scalable machine learning in a managed cloud enviornment.
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Python (for preprocessing, data analysis, machine learning, scripting) Data Warehousing (e.g., AWS Redshift, Google BigQuery) Apache Hadoop or Apache Spark (for big data processing.
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TensorFlow, PyTorch, Keras (for deep learning and complex machine learning, including neural networks and advanced AI) AWS, Google Cloud, or Azure (cloud platforms for scalable computing.
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CI/CD Pipelines (for automating deployment and monitoring of machine learning models) Experience with machine learning and statistical analyses are needed. Scikit-Learn (for classical machine learning.
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Natural Language Processing (NLP) Tools and Frameworks (e.g., Hugging Face, AWS Comprehend Medical for extracting insights from clinical text data) Docker, Kubernetes, ECS, EKS or AWS Fargate (for containerization and orchestration of data applications and reproducibility.
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Proficiency in programming languages such as Python, R, or SQL. Strong knowledge of machine learning techniques, statistical analysis, and data visualization tools Tableau, Power BI.
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Hands on experience with modern cloud data warehouse and engineering for one or more of the following cloud providers - GCP (Big Query, Vertex, Cognitive Services), Snowflake, Azure (Synapse, Dynamo, Cosmos, Azure ML, Power BI), AWS (Redshift, Sage maker), Tableau;Possessing a certification(s) in one of the following is a big plus – GCP/AWS/Azure/ data or ML engineering.
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5+ years of experience in data analysis or data science, with 3+ years focusing on machine learning problems, ideally in a relevant space (KYC, sanctions detection, anti-fraud detection, treasury management, crypto/blockchain data science.
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big data science machine learning google cloud jobs Company: Snowflake Computing
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