Associate Data Modeler, Decision Analytics, Greater China
ExperianShanghaiUpdate time: May 10,2019
Job Description
- Perform retail financial credit risk related analytics independently, which may include data cleaning/processing, credit risk model development, risk parameter modelling, core/advanced application, etc.
- Provide technical assistance to team members and conduct quality check when necessary.
- Prepare project related documents in Chinese or English.
- Conduct R&D with the aim of creating custom analytics solution and tackling technical issues.
- Support ma Understand and participate in performance management processes.
- Adopt a disciplined approach to do analytical work.
- Adhere with project design and timeline for assigned project delivery.
- Ensure high quality project delivery to satisfy external or internal clients.
- Contribute to analytical/consulting solutions in order to generate sustainable revenue. Actively contribute to a culture where the fair treatment of customers is at the heart of the Experian business.
- Take personal responsibility to ensure that you adhere to all regulatory requirements and apply appropriate controls in the interests of our customers.
- 1+ years’ analytical experiences in Retail Banking, Telecommunications, Insurance or other related industries. Data mining and Online banking development and implement experiences are preferred.
- Bachelor degree in a quantitative discipline such as Statistics, Econometrics, Operations Research or Mathematics. Master’s degree is preferred.
- Familiar with programming language in one, or more software such as Python, SAS, R, SQL. Experience with Python and either of the following modelling method: Machine Learning, Neutral Network, XGBoost are preferred.
- Ability to communicate fluently verbally and in written English.
- Passionate, Highly-motivated and prefer taking up new challenges.
- Proven ability to support the design and execution of custom analytic solutions.
- Capable of performing analytical work independently and cooperating with team members.
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