AI / ML Data Scientist (Across levels)
Tasks
- Build and deploy AI/ML models
- Collaborate with cross-functional teams to define requirements
- Conduct exploratory data analysis
- Deploy and monitor models with MLOps
- Develop classical machine learning models
- Document and communicate technical results
- Evaluate models with cross validation and tuning
- Optimize models for production
- Perform data preprocessing and feature engineering
- Research and improve model algorithms
Perks/Benefits
- N/A
Skills/Tech-stack
API Development | AWS | Apache Kafka | Apache Spark | Azure | CI/CD | CatBoost | Categorical Encoding | Cloud Computing | Component analysis | Computer Vision | Cross-validation | Data Preprocessing | Decision Trees | Django | Docker | Ensemble learning | Evaluation metrics | Feature Engineering | Fine Tuning | Flask | GCP | Git | Gradio | GridSearchCV | Hierarchical Clustering | Hyperparameter Tuning | Hypothesis Testing | K-Means | K-Nearest Neighbors | K-nearest | Kafka | Keras | Kubeflow | Kubernetes | Language Models | Language Processing | Large Language Models | LightGBM | Linear Regression | Logistic Regression | MAE | MLflow | Matplotlib | Missing data | Model Evaluation | Model Serving | Model evaluation metrics | Multimodal Learning | Natural Language | Natural Language Processing | Naïve Bayes | Nearest neighbors | Normalization | NumPy | Outlier Detection | Pandas | Principal Component Analysis | PyTorch | Python | RMSE | ROC-AUC | Random Forests | RandomizedSearchCV | Regression Analysis | Regularization | SQL | Scaling | SciPy | Scikit-learn | Seaborn | Statistical modeling | Streamlit | Support Vector Machines | TensorFlow | Testing | Transformers | Vector machines | Video Processing | XGBoost
Education
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