AI/ML Data Scientist | Onsite
Tasks
- Assess fairness explainability robustness and stability
- Build classification models for fraud churn and anomaly detection
- Build regression models for forecasting and risk scoring
- Collect clean transform analyze data
- Communicate model rationale and limitations
- Deploy models through APIs and pipelines
- Document methods data assumptions and results
- Engineer features and prepare data
- Evaluate models using performance metrics
- Monitor models in production and retrain
- Perform exploratory data analysis
- Select algorithms based on problem constraints
- Train validate test predictive models
- Translate business problems to predictive objectives
- Tune model hyperparameters with cross validation
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Airflow | Anomaly Detection | CI/CD | CNN | Classification | Computer Vision | Concept drift | Cross-validation | Data Analysis | Data Drift | Decision Trees | Docker | Elastic Net | Ensemble modeling | Exploratory Data Analysis | Fairness | Feature Engineering | GRU | Git | Google Cloud | Gradient boosting | Hyperparameter Tuning | K-Nearest Neighbors | K-nearest | Kubeflow | LSTM | Language Processing | Lasso Regression | Linear Regression | Logistic Regression | MLflow | Matplotlib | Microsoft Azure | Model Governance | Model Monitoring | Model retraining | Natural Language | Natural Language Processing | Naïve Bayes | Nearest neighbors | NumPy | Pandas | Privacy | PyTorch | Python | RNN | Random Forests | Recommender Systems | Regression | Responsible AI | Ridge regression | SQL | SciPy | Scikit-learn | Seaborn | Series forecasting | Spark | Support Vector Machines | TensorFlow | Time Series | Time Series Forecasting | Transformers | Vector machines | XGBoost
Education
Roles
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