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As a Director, Machine Learning Engineer you will partner with Data Scientists, Data Engineers, Data Analysts and other professionals to implement machine learning models that will deliver stability, producibility, scalability and integration with other products and services.
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You'll improve search latency and relevancy using large scale data systems and machine learning. You have experience in backend engineering, data infrastructure, search infrastructure or natural language processing/machine learning.
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PhD in Chemistry, Artificial Intelligence, Machine Learning, Data Science, Material Science, or a closely related field. Employ advanced AI and machine learning models, including deep learning and predictive analytics, to identify novel materials and predict their performance within battery systems.
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You are very skilled at using one or more BI tools (e.g. Tableau, Sigma, Looker), cloud data warehouses (e.g. Snowflake, Redshift, Big Query), data transformation frameworks (e.g. dbt), and data science tools (e.g. Python/Jupyter, Julia, R.
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Experience in Data science and Machine learning using Python, R, Java, C#, Spark, AutoML, TensorFlow, Amazon AML, Microsoft machine learning studio, PyTorch, IBM Watson and any graph DB.
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You will be involved in the design and development efforts for our big data solutions including data lake, Business Intelligence Solutions, Machine Learning, Data Pipeline, and cloud-based data warehouse products.
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Experience leveraging complex data to drive business decisions, hands on experience in data science methodologies (predictive analytics, machine learning, patient level data triggers) using R, Pytong, Databricks and deep knowledge of Qlik, PowerBI, Tableau for visualization.
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As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. We are a multidisciplinary team with expertise in machine learning, data engineering, and software development.
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Build end-to-end machine learning workflow, from data pipeline, modeling, tuning, evaluation, to continuous improvement, etc. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications.
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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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Data science, machine learning, optimization models, Master's degree in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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As a AWS Data Architect,you will beresponsible for leading a team of data engineers in the development andmaintenance of scalable data pipelines and implementing solutions in graphdatabases and machine learning.
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We are seeking talented engineers with a passion for building large-scale, distributed machine learning algorithms and systems that will transform the drug discovery process. gCS Prescient Design is seeking an exceptional Senior Machine Learning Engineer to develop our LLM and AI product and enable the next generation of foundational research in machine learning for scientific discovery.
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Familiarity with Databricks for data engineering tasks including ETL processes, data visualization, and machine learning. The ideal candidate will have a solid understanding of Java and Python programming languages, along with experience working with Databricks for data engineering tasks.
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SSIS strongly preferredPython DBT, Mulesoft, Alation – good to havePrimary Responsibilities:Strong understanding or Snowflake on Azure Architecture, design, implementation and operationalization of large-scale data and analytics solutions on Snowflake Cloud Data Warehouse.
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