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- Support the internal audit division to incorporate data analytics (DA) into various business and technology audits.
- Partner with business auditors to identify opportunities for advanced data analytics, robotic process automation, AI and Machine Learning to support continuous auditing and auditing initiatives.
- Support Continuous Audit (CA) rollout and the expansion of CA adoption, including implementation of Predictive Auditing in targeted areas
- Champion and promote the use of data analytics techniques to internal audit teams, including conducting internal training and sharing sessions.
- Work closely with business auditors on the development of DA use cases and dashboard to identify risk exposures, red flags and potential fraud.
- Support in strategic planning of internal audit DA and automation initiatives.
- Collaborate with country IA on advanced analytics projects and DA roll out.
- Possess a Bachelors' Degree in quantitative discipline (Computer Science, Engineering, Maths or Statistics)
- 10+ years of experience in IT or Banking industry with at least 2 to 3 years of working experience as a Data Scientist/ Data Analyst.
- Experience in applying data analytics in audit and compliance areas
- Knowledge of AS400 systems and related programming languages like RPG and COBOL will be added advantage
- Work experience in one or more of the following areas:
- Data analytics tools and programming languages such as SAS, Python, or R.
- Database management systems such as SQL, Oracle, Teradata, etc.
- Data visualisation tools, such as Power BI and QlikSense.
- Familiarity with HADOOP platform (MapR/ Hortonworks/ Cloudera, etc - Cloudera would be preferable),
- Data mining for processing, cleansing, and verifying the integrity of data used for data analytics.
- Knowledge of statistics and experience using statistical packages for analysing large datasets.
- Application of various machine learning techniques (Random Forest, Least Mean Square, Support Vector Machine, Natural Language Processing, XGBoost, etc).
- Good understanding of analytics modelling, business needs analysis and data visualization.
- Knowledge and experience in developing exception-based scripts and query reports to facilitate the identification of outliers/ anomalies/ trends
- Ability to leverage technology and new analytics techniques such as predictive, clustering and sentiment analysis to support data-driven insights and risk identification
- Support the internal audit division to incorporate data analytics (DA) into various business and technology audits.
- Partner with business auditors to identify opportunities for advanced data analytics, robotic process automation, AI and Machine Learning to support continuous auditing and auditing initiatives.
- Support Continuous Audit (CA) rollout and the expansion of CA adoption, including implementation of Predictive Auditing in targeted areas
- Champion and promote the use of data analytics techniques to internal audit teams, including conducting internal training and sharing sessions.
- Work closely with business auditors on the development of DA use cases and dashboard to identify risk exposures, red flags and potential fraud.
- Support in strategic planning of internal audit DA and automation initiatives.
- Collaborate with country IA on advanced analytics projects and DA roll out.
- Possess a Bachelors' Degree in quantitative discipline (Computer Science, Engineering, Maths or Statistics)
- 10+ years of experience in IT or Banking industry with at least 2 to 3 years of working experience as a Data Scientist/ Data Analyst.
- Experience in applying data analytics in audit and compliance areas
- Knowledge of AS400 systems and related programming languages like RPG and COBOL will be added advantage
- Work experience in one or more of the following areas:
- Data analytics tools and programming languages such as SAS, Python, or R.
- Database management systems such as SQL, Oracle, Teradata, etc.
- Data visualisation tools, such as Power BI and QlikSense.
- Familiarity with HADOOP platform (MapR/ Hortonworks/ Cloudera, etc - Cloudera would be preferable),
- Data mining for processing, cleansing, and verifying the integrity of data used for data analytics.
- Knowledge of statistics and experience using statistical packages for analysing large datasets.
- Application of various machine learning techniques (Random Forest, Least Mean Square, Support Vector Machine, Natural Language Processing, XGBoost, etc).
- Good understanding of analytics modelling, business needs analysis and data visualization.
- Knowledge and experience in developing exception-based scripts and query reports to facilitate the identification of outliers/ anomalies/ trends
- Ability to leverage technology and new analytics techniques such as predictive, clustering and sentiment analysis to support data-driven insights and risk identification