Data Scientist
The Versatile ClubIndia3 days ago
Full-timeEngineering, Information Technology
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Key Responsibilities
Machine Learning: Supervised/unsupervised learning, deep learning, NLP, computer vision, time series forecasting, and ensemble methods
Programming Languages: Python (scikit-learn, pandas, numpy), R for statistical analysis, SQL for data manipulation and analysis
ML Frameworks: TensorFlow, PyTorch, Keras, XGBoost, LightGBM, Hugging Face Transformers for model development
Data Processing: Advanced pandas, Apache Spark (PySpark), data wrangling, and large- scale data manipulation techniques
Statistical Analysis: Hypothesis testing, regression analysis, experimental design, and statistical modeling techniques
Visualization Tools: Matplotlib, Seaborn, Plotly, Tableau, Power BI for data exploration and results communication
Feature Engineering: Feature selection, dimensionality reduction (PCA, t-SNE), feature scaling, and data transformation techniques
Model Evaluation: Cross-validation, performance metrics, ROC curves, confusion matrices, and model interpretability (SHAP, LIME)
Database Technologies: SQL databases, NoSQL systems, and data warehouse querying for analytics and model training
Skills: feature engineering,data processing,ml,statistical analysis,programming languages,data,data visualization tools,model evaluation,ml frameworks,data science,machine learning
- Develop and validate predictive models and AI algorithms under the guidance of senior
- Perform data exploration, visualization, and feature selection for model development. 3. Implement classification, clustering, and forecasting models for government datasets. 4. Support AI/ML engineers in model training and evaluation activities.
- Create comprehensive documentation for models and data pipelines.
- Participate in performance testing and quality validation of AI outputs.
- Ensure alignment of data science processes with NeGD's Responsible AI principles.
Machine Learning: Supervised/unsupervised learning, deep learning, NLP, computer vision, time series forecasting, and ensemble methods
Programming Languages: Python (scikit-learn, pandas, numpy), R for statistical analysis, SQL for data manipulation and analysis
ML Frameworks: TensorFlow, PyTorch, Keras, XGBoost, LightGBM, Hugging Face Transformers for model development
Data Processing: Advanced pandas, Apache Spark (PySpark), data wrangling, and large- scale data manipulation techniques
Statistical Analysis: Hypothesis testing, regression analysis, experimental design, and statistical modeling techniques
Visualization Tools: Matplotlib, Seaborn, Plotly, Tableau, Power BI for data exploration and results communication
Feature Engineering: Feature selection, dimensionality reduction (PCA, t-SNE), feature scaling, and data transformation techniques
Model Evaluation: Cross-validation, performance metrics, ROC curves, confusion matrices, and model interpretability (SHAP, LIME)
Database Technologies: SQL databases, NoSQL systems, and data warehouse querying for analytics and model training
Skills: feature engineering,data processing,ml,statistical analysis,programming languages,data,data visualization tools,model evaluation,ml frameworks,data science,machine learning
Key Skills
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