Machine Learning with Python

Overview

On Site
$60,000 - $80,000
Full Time

Skills

ML Algorithms
Python
Software Engineer
Mathematical & Statistical
TensorFlow
PyTorch
Deep Learning
Data Handling & Analysis
AWS & GCP
MLops
NLP
Data Modeling

Job Details

Programming and Software Engineering:
  • Python Proficiency:
    Expertise in Python, including data manipulation libraries like Pandas and NumPy, is fundamental for building and deploying ML models.
  • Software Engineering Practices:
    Knowledge of version control (like Git), testing, and building scalable systems is essential for developing robust ML pipelines.
Mathematical and Statistical Foundations:
  • Linear Algebra:
    Understanding concepts like vectors, matrices, and linear transformations is crucial for understanding and implementing many ML algorithms.
  • Calculus:
    A grasp of calculus, particularly optimization techniques, is necessary for training and tuning ML models.
  • Probability and Statistics:
    A strong foundation in these areas is essential for understanding and interpreting data, building probabilistic models, and evaluating model performance.
Machine Learning Expertise:
  • ML Algorithms:
    Familiarity with various ML algorithms, including supervised, unsupervised, and reinforcement learning, is key.
  • Frameworks:
    Experience with popular ML libraries and frameworks like Scikit-learn, TensorFlow, and PyTorch is highly valuable.
  • Deep Learning:
    Knowledge of deep learning, including neural network architectures and training techniques, is increasingly important.
Data Handling and Analysis:
  • Data Preprocessing:
    The ability to clean, transform, and prepare data for ML models is crucial for achieving good performance.
  • Data Analysis:
    Strong data analysis skills are needed to gain insights from data and identify potential problems.
  • Data Visualization:
    The ability to visualize data effectively can help communicate insights and debug problems.
Additional Considerations:
  • Cloud Computing:
    Experience with cloud platforms (AWS, Azure, Google Cloud) is increasingly important for deploying and scaling ML models.
  • MLOps:
    Knowledge of Machine Learning Operations (MLOps) practices, including version control, testing, and deployment, is essential for building production-ready ML systems.
  • Natural Language Processing (NLP):
    A growing area of interest for many ML Engineers, particularly in domains like text analytics and chatbots.
  • Data Modeling and Evaluation:
    Understanding data modeling and evaluating model performance are crucial for ensuring that ML models are effective and reliable.
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