job details

EY is a global leader in assurance, tax, transactions and advisory services. Our 167,000 people are united by our shared values, which inspire our people worldwide and guide them to do the right thing, and our commitment to quality.

• Knowledge of Decision Analytics techniques;

• Solid understanding of Data Strategy , including data structure, data organization and access;

• Strong academic record; ideally a professional qualification, Master’s degree, MBA or PhD, from reputable institutions. Financial, Business, Economic,   Computer Science, Operations Research, Business Analytics, Statistics, Mathematics, Physics, Engineering or similar degrees are a significant advantage.

• Strong Analytics knowledge, including tools (SAS, SPSS, R, Hadoop) and methodologies;

• Excellent problem solving, project management, facilitation and interpersonal skills

• Team player with the ability to build effective relationships at all levels

• Willing and able to travel across the region 0-35% of the time

Experience

• Approximately 7+ years of data analytics and decision analytics

• The professional has to have experience in delivering decision analytics / Operations Research solutions based on techniques which could include some of   the following:

• Operations Research (simulation, optimisation, scheduling, inventory control, queuing theory, risk analysis, decision analysis, system dynamics, etc)

• Statistics (e.g. price elasticity analysis, forecasting, predictive analytics)

• Data mining, data visualisation and data management

• Database modelling including use of emerging Big Data technologies(Hadoop)

• Experience of analytics packages, including statistics packages such as SPSS or SAS, visualisation packages such as Tableau or Spotfire

• Strong knowledge of SQL and relational databases, as well as knowledge of predictive modelling technologies such as SAS Enterprise Miner, SQL Server   2005 DMX etc

• Technically competent in the following areas: data collection and load, data quality assessment, data cleansing, complex data transformation, relationship    profiling, sampling and extrapolation, statistical modelling, segmentation, structured data mining, text mining and analytical matching

• Has strong IT skills to programmer level, covering a range of different technologies relating to the techniques above