VP / AVP, Lead Data Science Analyst (Customer Science), Regional Consumer Banking Operations, Technology & Operations

  • Competitive
  • Singapour, Singapore
  • CDI, Plein-temps
  • DBS Bank Limited
  • 21 nov. 17 2017-11-21

See job description for details



Business Function

Group Technology and Operations (T&O) enables and empowers the bank with an efficient, nimble and resilient infrastructure through a strategic focus on productivity, quality & control, technology, people capability and innovation. In Group T&O, we manage the majority of the Bank's operational processes and inspire to delight our business partners through our multiple banking delivery channels.

Responsibilities

  • Lead the development and solutioning of the Customer Science Platform, focusing on real time predictive analytics to derive actionable insights to intervene positively into the customer’s digital journey
  • Manage project deliverables, mentor team members, build new capabilities, support/contribute to thought leadership and build out new skills within the team
  • Perform full life-cycle of Data Scientist / Analyst activities, including conceptualization to operationalization
  • Primary focus will be in applying data science to solve business problems; data mining techniques, doing statistical analysis, building high-quality prediction systems, and use deep learning techniques
  • Able to understand and solve the business problem by translating into a data model and building insights into an actionable outcome
  • Develop the abilities of a team of Data Scientists / Analyst / Visualizers and guiding them in the creation of high quality models, analytics, and visuals
  • Collaborate with cross-functional business teams to identify and prioritize actionable, high-impact insights across a variety of customer servicing areas
  • Research, design, implement and validate models / algorithms to analyse diverse sources of data to achieve targeted outcomes
  • Carry out customer behaviour analytics and deliver actionable insights in real time; through behaviour segmentation, predictive modelling, lifetime value modelling, churn prevention, statistical simulations and what if scenarios
  • Stay on top of current business and industry trends
Requirements
  • You are:
    • Curious and have a strong appetite for intellectual challenges. Able to pick up new methods and techniques quickly and apply towards solving a problem at hand
    • Keen on learning, data, scale and agility. You excel at making complex concepts simple and easy to understand by those around you. You’re driven to show the world the power of applied analytics
    • Passionate about asking and answering questions in large datasets, and you can communicate that passion to product managers and engineers
    • Attracted to a fast paced, hypothesis and test driven, collaborative and iterative engineering environment
    • Driven, strategically focused, self-starter and organized with strong attention to detail
    • A good team player with excellent communication skills
  • You have:
    • A university degree or higher in applied statistics, data mining, machine learning, computing or related quantitative discipline, with a strong background in statistical concepts and calculations
    • Proven ability on structured problem solving, data-driven analysis, real time analytics, and deriving actionable outcomes within the Financial Services domain
    • Proven track record of leading a team on handing big data and delivering impact to the business
    • Deep and practical understanding on implementing high performance, well-behaved analytics applications; data ingestion, feature engineering, model selection, training, validating and deployment
    • A deep understanding of statistical and predictive modelling concepts, machine-learning approaches, clustering and classification techniques, and recommendation and optimization algorithms
    • Relevant experience in the following:
      • Must have excellent Python, R and software development skills
      • Familiarity with Linux based operating system environments
      • Data engineering experience including real time data ingestion i.e. LogStash, Talend, Flume
      • Experience with scripting languages (e.g. Python, R, Julia) for data manipulation and statistical computing tools i.e. Spark Streaming (extraction, cleansing, transformation, smoothing, PMML model execution)
      • Experience in working with large datasets through OLAP tools i.e. Druid
      • Experience creating real time and rich data visualizations that involves large datasets i.e. Highcharts
      • Experience manipulating structured and unstructured data sources for analysis i.e. Greenplum, SparkSQL, HBase, S3 by using Notebook technologies such as Jupyter and Zeppelin
      • Working experience in cloud based and open source technology components
Apply Now

We offer a competitive salary and benefits package and the professional advantages of a dynamic environment that supports your development and recognises your achievements.