Explain how your experience in quantitative finance and Python coding prepares you for WorldQuant’s research workflow.
For this SQL exercise, use the supplied researcher, research run, and validation records to identify qualifying completed research runs.
researcher_name, run_id, submitted_at, oos_sharpe, prior_oos_sharpe, sharpe_change, validation_score, and leakage_breaches.oos_sharpe descending and researcher_name ascending as the tie-breaker.| Column | Type | Description |
|---|---|---|
| researcher_idPK | INT | Unique researcher identifier |
| researcher_name | VARCHAR(100) | Researcher display name |
| research_area | VARCHAR(100) | Primary quantitative research area |
| Column | Type | Description |
|---|---|---|
| run_idPK | INT | Unique research run identifier |
| researcher_id | INT | Researcher who submitted the run |
| submitted_at | DATE | Run submission date |
| status | VARCHAR(20) | Run processing status |
| python_version | VARCHAR(20) | Python version used by the run |
| Column | Type | Description |
|---|---|---|
| run_idPK | INT | Validated research run identifier |
| oos_sharpe | DECIMAL(6,3) | Out-of-sample Sharpe ratio |
| validation_score | DECIMAL(6,3) | Validation quality score |
| leakage_breaches | INT | Detected data leakage breach count |
| turnover | DECIMAL(6,3) | Portfolio turnover ratio |