Data Analyst, Manager

Overall Job Purpose:

Partner with specific client stakeholders within OCBC to understand their business challenges & use your data expertise to proactively identify areas where Customer Analytics techniques can create value.

Work closely with the Marketing team and product managers to design, develop, and implement campaigns.

Manage, track, and analyze marketing campaign performance for key customer insights, campaign optimization, and enhancement.

Represent GDO in cross-functional agile squads and business initiatives.

Support strategic build projects to continuously enhance the capability of OCBC’s analytical processes and big data platforms.

Roles & Responsibilities:

Generate Actionable Customer Insight to Improve Key Business Outcomes

Support the deployment of world-class marketing analytics capabilities and practices to improve marketing effectiveness and efficiency of the OCBC Global Wholesale Banking division. This includes:

1. Apply data and advanced analytics to drive relevance of the marketing opportunities presented to customers & front-line channels.
2. Conduct data discovery and other data exploration activities to identify emerging trends and business opportunities to grow the business.
3. Develop and manage predictive models, campaign reporting, analysis, and derivation of key customer metrics.
4. Execute marketing campaigns & event triggers to support customer acquisition, cross & upselling, and account activation goals within the Global Wholesale Banking business.
5. Provide ongoing support to segment and product management in the profiling and segmentation of customers as well as conduct tactical mining to support their business initiatives.
6. Regularly interact with product and segment managers to understand their business objectives and proactively recommend how analytics could help them achieve their goals.
7. Work closely with internal and external stakeholders in campaign design and promote automated solutions for execution efficiency.
8. Collaborate with the Data Engineering team to define the key pipelines of data required to support the business.
9. Collaborate with Data Scientists to develop predictive and recommendation engines for business overall acquisition, cross-sell, and usage strategies.