Data Scientist

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Proficiency in Python for data analysis, manipulation, and modeling.
Strong understanding of statistical concepts and methodologies for hypothesis testing, regression analysis, and predictive modeling.
Experience with machine learning techniques, including supervised and unsupervised learning algorithms, neural networks, and ensemble methods, using frameworks such as TensorFlow, PyTorch, or scikit-learn for building and deploying machine learning models
Familiarity with data visualization libraries such as Matplotlib, Seaborn, or ggplot2 for creating informative and visually appealing visualizations.
Familiarity with Python data science libraries such as Pandas, NumPy, and Scikit-learn for data manipulation, analysis, and modeling.
Knowledge of database systems and SQL for data retrieval, manipulation, and storage.
Understanding of big data technologies such as Hadoop, Spark, or Hive for processing and analyzing large volumes of data.
Proficiency in data preprocessing techniques such as data cleaning, feature engineering, and dimensionality reduction.
Experience with version control systems such as Git for managing code repositories and collaboration.
Ability to work with cloud computing platforms (e.g., AWS, Azure, Google Cloud) for data storage, processing, and deployment of machine learning models

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Post Date

2025-07-02

Job Type

Employment type

Full-time