Data Science and Optimization

Translate an investment mandate into an explicit universe, benchmark, rebalancing schedule, objective, and constraint set.

Retrieve prices, distributions, corporate actions, and security metadata from a documented data source using Python.

Query normalized price, corporate-action, and security-reference tables using SQL joins and window functions.

Construct total-return series from unadjusted inputs, or validate vendor-adjusted data, without double-counting distributions or splits.

Align multi-asset observations to a trading calendar without using future information.

Implement automated checks for missingness, duplicates, return outliers, adjustment errors, and universe changes.