I don't know what skleans models are. What I glean from your post is that you want to detect unusual activity in " daily given away" taking into account the total sales for a particular day for a particular manager. To do so one might need to take into account daily/weekly/monthly factors and perhaps holiday factors while dealing with level shift, trends and unusual values while incorporating memory (ARIMA structure). The idea is simple and profound .. identify typical response to assess/detect unusual activity via Intervention Detection. see this URL for an introduction http://docplayer.net/12080848-Outliers-level-shifts-and-variance-changes-in-time-series.html . His examples are purely univariate but the approaches have been easily extended to causal models . See this http://www.math.cts.nthu.edu.tw/download.php?filename=569_fe0ff1a2.pdf&dir=publish&title=Ruey+S.+Tsay-Lec1 where transfer function models are discussed and here for an overview http://autobox.com/cms/images/dllupdate/TFFLOW.png.
This can be accomplished by identifying/building a causal model this is normally called a Transfer Function model which is a combination of regression structure and time series featutes.
If you wish to post data for 1 example (real or contrived) I and others might be able to help further as this looks to me like very promising problem in search for a very promising solution leading to value. Essentially what you are trying to do is to convert data to information to action.
As a warning be aware that many regression type solutions premise data that is free of autocorrelation i.e. non-time series but that assumption is usually not disclosed . One has to be concerned with model assumptions and their validation.
Models need to be complex enough (fancy enough) but not too complex (fancy). Assuming that simple methods work with complex problems is not consistent with scientific method following Roger Bacon and tons of followers of Bacon.