The Analytics Officer Is Set To Initiate And Apply Data Science Best Practices For Developing Program Logics And/or Conducting Preliminary Tests And Analysis For Activities Such As But Not Limited To The Following
- Development of propensity/predictive models, credit risk models and business studies
- Integration of developed models to systems and internal processes within and outside the division
- Performance monitoring risk models and other data tools currently implemented
- Creation of various business dashboards, operating risk MIS and other adhoc reporting
- Mining, processing, and transformation of data needed for above-mentioned analytics activities
Major Responsibilities
- Risk Management Support : Develop, monitor and maintain Credit Risk Models, Risk MIS/Dashboard (i.e., Portfolio Dashboard, Collection Dashboard and other reports) used to effectively monitor and manage risk and minimize non-performing loans. Participates in the user acceptance test to ensure models are appropriately implemented not only within the direct system environment but also its relevant downstream environments.
- Business Decision Support: Conducts various studies and other analytics projects (descriptive/prescriptive models, segmentation analysis, etc .) that serve as tool for underwriting decisions and/or drive new insights for market expansion and/or policy refinements. Present results and insights to different stakeholders and recommendations on business initiatives that are worth pursuing.
- Service Delivery Management : Work plan and delivery schedule of all analytics projects assigned. This also includes ensuring all projects and regular tasks are deliver based on agreed timelines and business specifications
Qualifications
- Bachelor's Degree in Mathematical Sciences, Physics or Economics or Engineering
- Master's Degree in Mathematical Sciences, Physics or Economics or Engineering
- Decision Analytics / Data Science application, preferably in retail business environments.
- Analytical Skills
- Curious about the data and strong ability to identify unusual patterns and data variations
- Strong ability to link data to the business problem and/business solution
- Proficient in various data tools such as SAS, R, Phyton, Tableau and the likes.
- Advance skills in data programming and data manipulation
- Advance knowledge statistical analysis and statistical modeling (forecasting, linear and logistic regressions, random forest, and the like)
- Proficient in various data spreadsheets and story board presentation software
- Proficient in oral and written communication and can converse results and insights clearly to the business units
- Regularly look for opportunities to turn ideas into action; focuses on identifying new patterns, variations, and analogies to generate new ideas
- Results oriented and good project management skills
