Redcliffelabs
https://redcliffelabs.com/About
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Jobs at Redcliffelabs
Data Science
Machine Learning (ML)- Proficiency in Python: Expertise in Python, with hands-on experience using libraries like Pandas, NumPy, Scikit-learn, and TensorFlow.
- Machine Learning Algorithms: Strong understanding of supervised, unsupervised learning, and deep learning algorithms (e.g., regression, classification, clustering, neural networks).
- Data Preprocessing & Cleaning: Experience in handling and preprocessing large datasets, including cleaning, feature engineering, and data transformation.
- Model Deployment: Ability to deploy machine learning models into production using frameworks like Flask, FastAPI, or cloud platforms (AWS, GCP).
- Data Visualization: Proficiency in data visualization tools such as Matplotlib, Seaborn, or Plotly to communicate insights effectively.
- Statistical Analysis & Probability: Solid foundation in statistics, probability, hypothesis testing, and A/B testing for data-driven decision-making.
Database Management: Experience with SQL and NoSQL databases (e.g., PostgreSQL, MongoDB) for data retrieval and manipulation.
Data Science
PythonMachine Learning (ML)1. Proficiency in Python: Expertise in Python, with hands-on experience using libraries like Pandas, NumPy, Scikit-learn, and TensorFlow.
2. Machine Learning Algorithms: Strong understanding of supervised, unsupervised learning, and deep learning algorithms (e.g., regression, classification, clustering, neural networks).
3. Data Preprocessing & Cleaning: Experience in handling and preprocessing large datasets, including cleaning, feature engineering, and data transformation.
4. Model Deployment: Ability to deploy machine learning models into production using frameworks like Flask, FastAPI, or cloud platforms (AWS, GCP).
5. Data Visualization: Proficiency in data visualization tools such as Matplotlib, Seaborn, or Plotly to communicate insights effectively.
6. Statistical Analysis & Probability: Solid foundation in statistics, probability, hypothesis testing, and A/B testing for data-driven decision-making.
7. Database Management: Experience with SQL and NoSQL databases (e.g., PostgreSQL, MongoDB) for data retrieval and manipulation.
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