Senior Data Analytic Manager
About Standard Chartered
We are a leading international bank focused on helping people and companies prosper across Asia, Africa and the Middle East.
To us, good performance is about much more than turning a profit. It's about showing how you embody our valued behaviours - do the right thing, better together and never settle - as well as our brand promise, Here for good.
We're committed to promoting equality in the workplace and creating an inclusive and flexible culture - one where everyone can realise their full potential and make a positive contribution to our organisation. This in turn helps us to provide better support to our broad client base.
Scope of the Role:
Standard Chartered Bank (SCB) is a leading international bank operating in some of the most dynamic markets in the world, in Asia, Africa and the Middle East. Our successful and sustainable business is built on doing tangible, long term good for our people, our customers and the communities we serve.
The Data Solution Delivery team under Technology & Innovation Team in SCB China is founded to render the data solution service to the SCB China by leverage the country data warehouse - EDMpCN.
As Data Analytic Manager of Data Solution Delivery, he/she is required to provide end to end service to the Bank for Technology Solution for Regulatory Reports, Management Information Reports, Data Analytics and Data Services. Key Responsibilities:
Our Ideal Candidate Educational Level:
- Drive the Bank's technology innovation activities with a focus on Data science and analytics;
- Analyse, design, build, and document technology solutions required in all Bank segments (Retail, SME, and Priority & Private, commercial banking, and corporate & institutional banking) by leverage the latest data technologies;
- Provide solution and support to the banks' regulatory reports and management information reports;
- Support the operation of the country data warehouse, including architecture roadmap and daily operation;
- Drive data quality control is in line with data governance guideline and meet the downstream applications' requirement on data accuracy, timeliness and completeness.
- Master or Bachelor Degree in computer science, engineering, math, physics or related fields
- Business English (fluent verbal and written)
- Chinese (fluent verbal and written)
- Proven ability to draw insight, influence others for a common goal in a large organization.
- Deep knowledge and proven statistical analysis ability in solving real-world cases
- Hands-on experience in analysis of complex data problems
- Experience in driving innovative business solutions
- Experience in working with large volumes of data from multiple systems; understand data structure and draw insights. Digital project experience/exposure.
- Good understanding of the Financial products in the retail & wealth management area, engineering (product set up), booking model and process, regulatory backgrounds, valuation. In-depth knowledge of structured or derivatives and securities instruments, the underlying cashflow, valuation, trading and settlement is a plus.
- 8+ years of above experience
Computer Software/Languages/Other Requirements:
- Stakeholder management and communication skill
- Critical thinking skills
- Must have strong sense of ownership
- Must be an effective communicator with business English literacy, interpersonal, problem solving and analytical skills, and excellent communication and presentation skills
- Deep understanding of quantitative and qualitative analysis skills, classifying, predicting, modeling, forecasting
- Deep understanding of data architecture and modeling technique
- Project management experience for mid-large scale technology projects
- Good understanding of data tools such as Database Query Language (SQL), Java/Python
- Solid experience with BI tools such as SAS, Tableau, Microstrategy, Power BI etc.
Nice to have
- Experience with Hadoop platform, familiar with HiveQL, Spark MLLib. MapReduce, HBase. Hive. Shell scripting.
- Machine learning, deep learning, AI skills/exposure a plus
- Familiar with machine learning classification, regression, clustering and filtering etc.
- Familiar with statistical tests, distributions, maximum likelihood estimators, etc. solid statistics knowledge to understand when different techniques are (or aren't) a valid approach
- Familiar with machine learning methods, i.e.k-nearest neighbors, random forests, ensemble methods, and more. Understand when it is appropriate to use different techniques
Apply now to join the Bank for those with big career ambitions.
To view information on our benefits including our flexible working please visit our career pages .