Adp, Inc.

Min Xiao

Xiaojing Wang

Dmitry Alexandrovich Tolstonogov

Lei Xia

Marc Rind

Vikas Saini

James R Haas

Samir Khanal

Eric Zitaner

Chandradeep Kanakamedala

Sachin V Havaldar

Anshuman Gaur

Michael Ratcliff

Manoj Oleti

Nitin Kumar Tammewar

Uday Kovur

Faseeha Khan

Slawomir Krysiak

Linxuan Jiang

Fang Yuan

Manish Karanjavkar

Managing and applying human resources data comprising aggregating employee transaction data for an organization. A number of human resources-related attributes are evaluated across heterogeneous transaction data. The employee transaction data is classified via statistical machine learning into a number of normalized codes according to the human resources-related attributes, a user interface is presented to adjust a number of organizational operating procedures according to the normalized codes.

Earning code classification

A method for predicting changes in customer demand is provided. The method comprises collecting subscription data for a number of customers at specified time intervals, wherein each customer is subscribed to one of a number of defined bundles of services. Any changes in customer bundle subscriptions during a given time interval are determined along with metrics for defined customer tasks for subscribed services during the given time interval. Multimodal multi-task learning is used to simultaneously model both bundle subscription change events and time-to-event for each bundle subscription change. Using the modeling, types and timing of changes in customer bundle subscriptions are predicted based on customer service activities.

Multi-task deep learning of customer demand

A method, computer system, and computer program product for determining job levels of an employee within an organization. A computer system identifies employee data for the employee. The computer system determines whether the employee is a manager or a non-manager based on the employee data for the employee. The computer system applies a manager employee level model to determine a manager job level of the employee in response to determining that the employee is a manager. The computer system applies a non-manager employee level model to determine a non-manager job level of the employee in response to determining that the employee is a non-manager.

Job level prediction

Display screen or portion thereof with animated graphical user interface

Display screen or portion thereof with graphical user interface

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Caldwell, NJ, United States of America

Min Xiao