Data Scientist- Hybrid (3 times per week)
Tasks
- Apply clustering and segmentation techniques
- Apply cross validation and handle class imbalance and calibration
- Build and validate time series models with backtesting
- Build feature pipelines including aggregation encoding and scaling
- Collaborate with stakeholders on success metrics and constraints
- Create evaluation reports and documentation
- Extract join and analyze data using SQL
- Implement MLOps including versioning automated evaluation monitoring and retraining
- Monitor model drift and performance
- Package models for deployment batch scoring or real time APIs
- Perform data profiling missingness analysis and leakage checks
- Perform error analysis with SHAP and cohort slicing
- Train and tune supervised learning models
- Translate business questions into ML problem statements
Perks/Benefits
- N/A
Skills/Tech-stack
AUC | AWS | Airflow | Anomaly Detection | Azure | BigQuery | CatBoost | Class imbalance | Classification | Clustering | Cross-validation | DBSCAN | Data Leakage Prevention | Data leakage | Data profiling | Databricks | Delta Lake | Docker | F1-score | Feature Engineering | Gaussian Mixture Models | Google Cloud | Handling Class Imbalance | K-Means | Keras | Leakage prevention | LightGBM | MAE | MAPE | MLOps | MLflow | Missing data | Mixture models | Model Drift | Model Monitoring | Model calibration | NumPy | Pandas | Precision-recall | PyTorch | Python | RMSE | Redshift | Regression | SHAP | SQL | Scikit-learn | Series forecasting | Snowflake | Spark | TensorFlow | Time Series | Time Series Forecasting | XGBoost
Education
N/A
Regions
Countries
States
Cities
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