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Familiarity with frameworks and libraries relevant to each domain, such as Django or Flask for Python web applications; React, Angular, or Vue.js for front-end web development; TensorFlow or PyTorch for AI; and React Native, Flutter, or native development kits for mobile applications.
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Proficiency in Python, and experience with deep learning frameworks such as PyTorch, Tensorflow/Keras, MXNet. We're seeking a highly skilled and motivated AI/ML Quantitative Analyst who's adept at advanced Generative AI development and implementations to join our AI/ML Center of Excellence in Model Risk Management.
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Experience with one or more general purpose programming languages including but not limited to: Python, R, Scala, Spark. Development experience with AI frameworks such as TensorFlow, Microsoft CNTK, scikit, Keras, Caffe, Gluon, Torch.
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Programming:Proficiency in Python and familiarity with key data science and machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn). OverviewTransaction Network Services (TNS), a subsidiary of Koch Industries, is seeking a skilled Senior Data Scientist to join our AI Labs. This role involves analyzing complex datasets, developing statistical and deep learning models, and implementing advanced machine learning solutions.
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Preferred skills: NLP, Text mining, Tableau, PowerBI, SAS, Tensorflow. Knowledge of Statistics, SAS, Python, Computer Vision, data visualization tools. entry-level software programmers, Java Full stack developers, Python/Java developers, Data analysts/ Data Scientists, Machine Learning engineers.
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Strong expertise in machine learning frameworks (e.g., TensorFlow, PyTorch) and programming languages (e.g., Python, R). Strong expertise in machine learning frameworks (e.g., TensorFlow, PyTorch) and programming languages (e.g., Python, R.
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Must possess 2 years of experience in each of the following: building and maintaining machine learning models for fraud prevention across consumer banking and credit card products; developing machine learning models to solve business problems; SQL, Hive, Python, Scala, Spark, Tensorflow, and Hadoop.
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Proficiency in Google Cloud Platform (GCP) services relevant to machine learning and AI, such as AI Platform, BigQuery, Dataflow, and Tensorflow. Expertise in open source data science technologies such as Python, R, Spark, SQL.
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Experience with Agile Software Development Lifecycles and DevOps/DevSecOps. Experience with one or more of the following: Natural Language Processing, sentiment analysis, classification, pattern recognition.
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Knowledge of Machine Learning / Deep Learning, including Python libraries like TensorFlow and PyTorch. Hands-on experience using data analytics and optimization tools (i.e., SQL, Python, R, Gourbi, Vertex AI.
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Solid experience with software engineering best practices, command line, version control (Git), and infrastructure as code (AWS CDK or Terraform), and proficiency in Python. · Experience with standard machine learning tools, libraries, and infrastructure solutions such as Scikit-learn, Pandas, PyTorch, TensorFlow is a plus.
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Strong coding skills in Python and ML frameworks (e.g., TensorFlow, PyTorch). We work with companies funded by the best investors including Sequoia Capital, Lightspeed Ventures, Tiger Global Management, A16Z, Accel, DFJ, and more.
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Strong software development skills, with proficiency in relevant programming languages and AI/ML frameworks (e.g., Python, TensorFlow Lite). The Machine Learning Engineer will be responsible for software development, optimizing hardware and infrastructure, designing data pipelines, and implementing robust AI solutions for real-world use cases.
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As a Machine Learning Engineer, you will be a driving force in shaping our ML infrastructure and capabilities. The primary function of the Machine Learning Engineer is to define processes, establish infrastructure, and contribute to enhancing the Machine Learning capabilities of the organization from the ground up.
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Defining processes and best practices to establish the Machine Learning infrastructure at Nomo, contributing to the evolution and enhancement of our ML capabilities. Hands-on experience in successfully shipping machine learning-based projects into production and delivering tangible impact to customers.
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The summer had economists from around the globe embroiled in a debate about a possible recession coming in the next few years (or months). As of October 2022, the U.S. Labor Department data put the current inflation rate at 7.7%. The recent layoffs in the tech industry are just the first of what is soon to be a string of cutbacks by companies looking to save costs. For recruiters, this means freezes in hiring and fewer openings. It will also include the uphill task of finding the best candidates for them from the coming influx of recently laid-off job seekers. Now is probably a good time to brace for tough times in the next few years in the talent acquisition industry. To survive and thrive recruiting in a recession, here are some hard truths you will need to accept.
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