Advanced Analytics & Data Science Manager
- Leveraging on social media data and other possible data set, the key responsibilities of the Advanced Analytics and Data Science Manager are:
1) To apply sophisticated statistical and/or technological tools to deliver actionable insights that clearly addresses clients’ needs, objectives and challenges.
2) To translate highly technical and complex data into easy-to-understand insights and visualization for general business audience.
3) To continuously drive innovation and exploratory experimentation in developing new and highly-applicable business-driven solutions in the big data environment.
- The scope of work will include but not limited to: data mining, predictive modelling, statistical analysis, data visualization, web analytics, entity extraction, natural language processing, machine learning and textual analytics.
- The Manager will need to apply extreme flexibility and creativity in the use of data to achieve stated objectives directly or indirectly.
- The Manager will also need to develop analytical solutions that have commercial viability and strong business acumen.
- Postgraduate or Master degree in Business Analytics. Other related programs such Computer Science or Data Science with additional background in Business will be advantageous.
- Minimum 2-4 years’ experience in market research, analytics or social media research industry
- Excellent analytical and logical skills with a strong business acumen – eye for details, ability to make sense of data and ask the “what does this mean?” for businesses.
- Good time and project management skills with ability to coordinate manage and influence teams directly and indirectly.
- Good delivery skills in report writing and presentation, with ability to deliver technical discussion into layman conversation.
- Attitude of a “problem solver, solution provider”, highly-motivated, results-driven, receptive to dynamic changes and willing to undertake continuous self-development in areas relevant to the role.
- Experience in using statistical, analytical, visualisation and programming tools such as (but not limiting to): SPSS, SAS, Tableau, Qlikview, Power BI, Python, R, VBA (Macros).