Total Rewards Intern

Job description

The internship aims to provide the following

Learning and experience on global and strategic total rewards programs, processes and projects. Understanding of the different rewards (compensation and benefits) practices, policies of various countries and what influences them. Understand the importance of rewards function and its value under the HR function umbrella and on the whole spectrum of the employee experience, consequently to the business strategy. An enriching internship experience where they can apply their academic learning in both practical day to day work, in a cross-cultural, diverse and flexible work environment.

Apply analytic tools in different areas of the work that they will be involved in.

Responsibilities

Coordinates requirements and provides support to TR team on Phantom Stock Option (PSO) regular reporting and annual nomination process, Salary Review Planning and Budget Annual Planning process, and Global Mgt. Incentive (MIP) process. Rewards projects ie. Career architecture, International Mobility (IM) set-up, particularly on data simulations and modelling, project monitoring, coordination with different stakeholders and external partners, as needed.

Conducting research studies related to rewards – policies, programs, trends, market data, rewards practices. Preparation of various compensation-related analysis for different countries and in different regions.

Qualifications

Preferably students from Bachelor & Master courses specializing in Economics, Engineering, Finance, Data Analytics, Market Research, Human Resources. Priority will be given to those who can commit internship for at least for 6 months period. Good inclination on finance, market research and data analytics. Has knowledge on using MS Office, Advance Excel, Power BI, and an advantage to have knowledge and skills in Python, and other data analytics tool.

Strong analytical skills, astute and meticulous. Good inter-personal and communication skills. Can work well under pressure Numbers-driven and analytical, keen attention to details, able to manage large sets of data.