In KPMG's Management Consulting practice, we do not limit ourselves to either strategy or implementation. Instead, we deliver both. Our team in Hong Kong is the fastest-growing division within KPMG China and represents a young and enthusiastic team that always pushes itself to succeed. Since our creation, we have developed an in-depth body of knowledge of an incredibly broad spread of sectors and services.

Our Data & Analytics team focuses on assisting clients to execute their strategy by optimising the use of information. We enable businesses to realise their objectives through insights from the data and provide the right information to the right people at the right time. The role of a Manager within KPMG has three specific areas of focus:

Business Development

- Building strong and lasting client relationships through excellent interpersonal skills
- Working with Senior Management and Leadership to develop compelling propositions which target relevant client issues
- Identifying project opportunities and supporting the development of proposals 
- Taking an active role in managing initiatives to appropriately market Data & Analytics capabilities and services 

Client Service

- Delivery of high quality advisory and/or technical services to our clients
- Planning, managing and executing the delivery of engagements or projects, including responsibility for the review and quality assurance of deliverables
- Building lasting relationships with key client stakeholders
- Identifying and managing risks whilst maximising profitability and ensuring the Firm's frameworks are implemented appropriately and effectively

Practice Development

- Utilising previous and ongoing experience to build and develop knowledge within the team
- Developing and building on both consulting and delivery capabilities
- Supporting key training and sharing knowledge within team
- Helping our junior team members to develop through mentoring and monitoring in the role of a Performance Manager and by disseminating experience throughout the team

As part of this team, you will be providing specialised consulting services in Data Warehousing and Reporting, including solution design, solution development and system implementation, to clients across different industries.

The individual will have:

- Bachelor's degree, preferably in Statistics, Mathematics, Econometrics or Quantitative Research
- 6+ years experience relating to Data & Analytics, with at least 3 years in statistical modelling, data mining or data science related work
- Proficiency and working experience with at least one statistical modelling tool (such as SAS, SPSS, Matlab or Alteryxx)
- Good knowledge of at least 1 statistical/data science language and analytics module (such as R, Scala or SQL)
- Proven delivery experience in modelling / data science / quantitative analytics projects and work
- Experience with data visualisation techniques and modules (such as R3, R, Python, etc.) preferred
- Good understanding of the principles and practices of statistical modelling, data science, data mining and quantitative analytics
- Retail or Financial Services (Banking, Insurance, Asset Management) industry experience preferred
- Experience working in a consulting environment preferred
- Strong analytical mindset with solid problem solving skills and ability to analyse complex data, identify core issues and investigate, evaluate and reach appropriate conclusions/solutions
- Proven project management skills including planning, designing and shaping project work streams, managing progress, resolving issues and limiting risks
- Good English language skills; Mandarin skills preferred but not required

We offer successful candidates an attractive remuneration package and the opportunity to work in a dynamic and exciting environment.

Personal data collected will be used for recruitment purposes only.

© 2017 KPMG, a Hong Kong partnership and a member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative ("KPMG International"), a Swiss entity. All rights reserved.