Creates machine learning models and utilizes data to train models Focuses on analyzing data to find relations between the input and the desired output Understands business objectives and develops models that help achieve them along with metrics to track their progress Designs and develops machine learning and deep learning systems Runs machine learning tests and experiments Implements appropriate machine learning algorithms
General Skills:
Experience managing available resources such as hardware data and personnel so that deadlines are met
Experience analyzing the machine learning algorithms that could be used to solve a given problem and ranking them by their success probability
Experience exploring and visualizing data to gain an understanding of it then identifying differences in data distribution that could affect performance when deploying the model in the real world
Experience verifying data quality and/or ensuring it via data cleaning
Experience supervising the data acquisition process if more data is needed
Experience finding available datasets online that could be used for training
Experience defining validation strategies
Experience defining the preprocessing or feature engineering to be done on a given dataset Background in statistics and computer programming
A team player with a track record for meeting deadlines managing competing priorities and client relationship management experience
Requirements
Experience and Skill Set Requirements
(15%) Deep Understanding of Machine Learning Concepts: Proficiency in fundamental machine learning concepts algorithms and techniques.
Expertise in Natural Language Processing (NLP): Knowledge of NLP techniques and models especially BERT and other transformer-based models for tasks like text classification sentiment analysis and language understanding.
(20%) Experience with Deep Learning Frameworks: Proficiency in deep learning libraries such as TensorFlow or PyTorch. Experience with implementing training and fine-tuning BERT models using these frameworks is crucial.
(30%)Data Preprocessing Skills: Ability to perform text preprocessing tokenization and understanding of word embeddings.
Programming Skills: Strong programming skills in Python including experience with libraries like NumPy Pandas and Scikit-learn.
(20%) Model Optimization and Tuning: Skills in optimizing model performance through hyperparameter tuning and understanding of trade-offs between model complexity and performance.
(15%) Understanding of Transfer Learning: Knowledge of how to leverage pre-trained models like BERT for specific tasks and adapt them to custom datasets.
Must Haves:
Deep Understanding of Machine Learning Concepts: Proficiency in fundamental machine learning concepts algorithms and techniques.
Expertise in Natural Language Processing (NLP): Knowledge of NLP techniques and models especially BERT and other transformer-based models for tasks like text classification sentiment analysis and language understanding.
Experience with Deep Learning Frameworks: Proficiency in deep learning libraries such as TensorFlow or PyTorch. Experience with implementing training and fine-tuning BERT models using these frameworks is crucial.
Data Preprocessing Skills: Ability to perform text preprocessing tokenization and understanding of word embeddings.
Programming Skills: Strong programming skills in Python including experience with libraries like NumPy Pandas and Scikit-learn.
Model Optimization and Tuning: Skills in optimizing model performance through hyperparameter tuning and understanding of trade-offs between model complexity and performance.
Understanding of Transfer Learning: Knowledge of how to leverage pre-trained models like BERT for specific tasks and adapt them to custom datasets.
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