Data Scientist – Python, ML & Predictive Modeling
About the role
Data Scientist – Python, ML & Predictive Modeling Duration: 12 months Work Model: 100% Remote Programming & Tools Strong proficiency in Python (NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch). Working knowledge of statistical analysis and modeling. Experience with Jupyter Notebooks, RStudio, and data visualization tools. Familiarity with SQL and data querying. Machine Learning & AI Solid understanding of supervised and unsupervised machine learning algorithms. Hands-on experience with: Regression (Linear, Logistic) Classification (Decision Trees, Random Forests, SVM) Clustering (K-Means, Hierarchical) Experience with deep learning frameworks such as TensorFlow and PyTorch is a plus. Predictive Modeling Proven experience in predictive modeling and forecasting. Ability to build, validate, and deploy predictive models. Strong understanding of: Feature engineering Model evaluation techniques (ROC, Precision/Recall, Cross-Validation) Experience working with real-world datasets to derive actionable insights. Statistics & Data Analysis Strong foundation in statistics and probability. Experience with hypothesis testing, regression analysis, and statistical modeling. Proficiency in data cleaning, transformation, and exploratory data analysis (EDA). Data & Deployment (Preferred) Experience with cloud platforms such as AWS, Azure, or GCP. Familiarity with Docker and containerization is a plus. Exposure to MLOps practices and CI/CD for machine learning models. Soft Skills Strong analytical and problem-solving skills. Ability to translate business problems into data-driven solutions. Effective communication and storytelling with data. Collaborative mindset with cross-functional teams. Nice-to-Have Experience with big data technologies such as Spark and Hadoop. Exposure to NLP, computer vision, or time-series forecasting. Knowledge of model deployment APIs such as Flask and FastAPI.
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Data Scientist – Python, ML & Predictive Modeling
About the role
Data Scientist – Python, ML & Predictive Modeling Duration: 12 months Work Model: 100% Remote Programming & Tools Strong proficiency in Python (NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch). Working knowledge of statistical analysis and modeling. Experience with Jupyter Notebooks, RStudio, and data visualization tools. Familiarity with SQL and data querying. Machine Learning & AI Solid understanding of supervised and unsupervised machine learning algorithms. Hands-on experience with: Regression (Linear, Logistic) Classification (Decision Trees, Random Forests, SVM) Clustering (K-Means, Hierarchical) Experience with deep learning frameworks such as TensorFlow and PyTorch is a plus. Predictive Modeling Proven experience in predictive modeling and forecasting. Ability to build, validate, and deploy predictive models. Strong understanding of: Feature engineering Model evaluation techniques (ROC, Precision/Recall, Cross-Validation) Experience working with real-world datasets to derive actionable insights. Statistics & Data Analysis Strong foundation in statistics and probability. Experience with hypothesis testing, regression analysis, and statistical modeling. Proficiency in data cleaning, transformation, and exploratory data analysis (EDA). Data & Deployment (Preferred) Experience with cloud platforms such as AWS, Azure, or GCP. Familiarity with Docker and containerization is a plus. Exposure to MLOps practices and CI/CD for machine learning models. Soft Skills Strong analytical and problem-solving skills. Ability to translate business problems into data-driven solutions. Effective communication and storytelling with data. Collaborative mindset with cross-functional teams. Nice-to-Have Experience with big data technologies such as Spark and Hadoop. Exposure to NLP, computer vision, or time-series forecasting. Knowledge of model deployment APIs such as Flask and FastAPI.