Very strong SAS, SQL, data modelling and transformation skills - with ability to manipulate large data sets, automate processes and debug code with minimal steer.
-Strong passion for data exploration, manipulation, data quality and bench marking utilizing SAS or any other analytical tools
-Become data and process experts across different clusters feeding information into the DBM models.
-Excellent knowledge of data warehouse and MI environments
- Knowledge discovery and presenting findings for range of stakeholders. This involves importing, cleaning, transforming, validating and bench marking or modelling data with the purpose of understanding or making conclusions from the data for decision making purposes
- End-to-end project planning and delivery. Ability to work dynamically on multiple projects independently and strong stakeholder management while putting customers in the heart of everything we do
- Validate, track, and monitor delivered projects.
- Upkeep of BAU processes by ensuring regular review of DBM data preparation processes to identify areas of improvement, focusing on accuracy, robustness and controls
- Produce robust documentation to ensure reliability of results
Essential Skills :
- A high level of analytical work experience in financial services strongly preferred.
- Extensive knowledge of SAS, Rational Databases (Oracle, Teradata etc.), UNIX, Tivoli and other analytic toolsets.
- Experience in consumer credit risk and/or finance analytics across customer lifecycle
- Knowledge of financial services portfolios. Awareness of the economy, market and customer trends affecting the business.
- Familiarity with analytical techniques and their value in business.
- Strong communication and interpersonal skills.
Preferred Skills :
- Experience in working on Hadoop platform and tools such as Impala, Hive, Spark, Pig, etc. is a bonus
- knowledge of Collections and Recoveries in customer life cycle
- Experience with other analytical tools such as R, WPS and SPSS
Knowledge of SAP Business Objects is desirable but not essential
- Familiar modelling (PD, LGD and EaD) and Regulatory reporting
- University degree in quantitative discipline is an advantage (e.g. Computer Science, Statistics, Operations Research, Economics and Engineering)