Responsibilities

• 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:

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.

Skill Set Requirements:

• 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.