Understand business objectives and develop models that help to achieve them, along with metrics to track their progress; Apply your advanced understanding of machine learning principles, including advanced analytics tools, applied mathematics, ML and Deep Learning frameworks and libraries, and ML techniques to design, build and support machine learning to achieve high quality generative AI user experiences; Analyze the ML algorithms that could be used to solve a given problem and rank them by their probability of success; Explore and visualize data to understand it, then identify differences in data distribution that could affect performance when deploying the model in the real world
Requirements:
Bachelor’s or Master's degree in Computer Science, Statistics, Data Science, Artificial Intelligence, or a related field; Proficiency with a deep learning framework such as PyTorch, TensorFlow, or Keras and and/or natural language processing using NLTK, spaCy, Gensim, etc; Proficiency in Python and basic libraries for machine learning such as scikit-learn and Pandas, as well as Jupyter Notebooks for all sorts of data science tasks
Text:
Senior Machine Learning Engineer/Data Scientist Understand business objectives and develop models that help to achieve them, along with metrics to track their progress; Apply your advanced understanding of machine learning principles, including advanced analytics tools, applied mathematics, ML and Deep Learning frameworks and libraries, and ML techniques to design, build and support machine learning to achieve high quality generative AI user experiences; Analyze the ML algorithms that could be used to solve a given problem and rank them by their probability of success; Explore and visualize data to understand it, then identify differences in data distribution that could affect performance when deploying the model in the real world Bachelor’s or Master's degree in Computer Science, Statistics, Data Science, Artificial Intelligence, or a related field; Proficiency with a deep learning framework such as PyTorch, TensorFlow, or Keras and and/or natural language processing using NLTK, spaCy, Gensim, etc; Proficiency in Python and basic libraries for machine learning such as scikit-learn and Pandas, as well as Jupyter Notebooks for all sorts of data science tasks
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