VP, Data Scientist, Institutional Banking Group Operations, Group Technology & Operations
DBS Bank Limited
Location: Singapore, Singapore
Type: Full Time
Internal Number: 12337896
Business Function Corporate and Investment Banking provides corporate customers with a full range of commercial banking products and services, including cash management services, current accounts, time deposits, trade finance, working capital finance, term loans and foreign exchange. Through our continued commitment, DBS has cultivated long-standing relationships with its customers in the region that are based on account relationship management, service differentiation, product development and rigorous credit standards. About this role: The Regional Data team is part of Institutional Banking Group Operations (IBGO) and our responsibilities include building the Data Science Solutions using advanced machine learning methods, recommendation engines, text mining solutions and customer models to amplify the business impact and efficient customer management, as well as answer numerous business questions using statistical tools and data analysis for Institutional banking customers. The Data Scientist will lead a team of data analysts in maintaining Data related projects and BAU operational pipelines. This role will oversee and be responsible for the recurring execution of data science pipelines and the ongoing refinement of machine learning models to incorporate new feedback. The Data Scientist will also work with technology partners to promote stable pipelines into production. Responsibilities:
Maximize Data capabilities focusing on customer/ employee science in areas such as predictive/ prescriptive/text analytics across corporate banking products/ processes and customer servicing (Chatbot & other digital channels).
Deliver analytics initiatives to address business problems with the ability to determine data required, assess time & effort required and establish a project plan. To manage other digital instrumentation projects undertaken by the team, and be accountable for their delivery (includes planning, prioritization, scope, risk/ issues management)
Conduct strategic data analysis, identify insights and implications from institutional data and make strategic recommendations to executive and senior staff, develop data displays that clearly communicate complex analysis
Test the solution with real live data & present the results to senior management for approvals
Prepare project updates report and communicate projects benefits & progress to management team or business stakeholders
Engage with the broader analytics community within the bank and align with the overall strategy / direction undertaken by the bank including reusable assets - both creation as well as reuse of existing assets.
Responsible for recurring execution of data science pipelines, including resolution of any issues to ensure its successful & timely completion.
Implement refinements to promote pipeline robustness and resiliency, as well as any optimizations required to improve software engineering.
Monitor model statistical performances to ensure that these continue to be performant and stable.
Gather and analyse feedback to incorporate these as part of ongoing model refinement.
Work with technology partners to promote stable pipelines into production, e.g. facilitating knowledge transfers, refactoring and testing as required.
Provide project leadership during the experimentation and implementation phase in close working with analytics/data translators and technology teams
PhD or Masters or equivalent degree in Statistics, Applied Mathematics, Operation Research, or equivalent quantitative fields preferred. Strong mathematical and statistics background.
6-8 years of experience in industry (ideally banking, ecommerce, telecoms, retail) and/or academia with demonstrated track record of innovative research and insight generation and implementation of insights into tools/processes delivering front end business result
At least 5 years of data mining and machine learning on large amount of data, building and implementing various statistical models.
Good understanding of technology tools especially those related to analytics, data & statistical modelling.
Have developed and implemented industrial standard machine learning solutions for classification, prediction, text mining and anomality detection problems.
Familiarity with a wide range of statistical analysis, machine learning, natural language processing and deep learning techniques.
Expertise in machine learning and data mining with excellent data processing, wrangling and feature engineering skills. Familiarity with industry paradigms and standards for model development, validation and testing.
Strong theoretical understanding and practical knowledge and be able to apply the appropriate solution framework for different modelling tasks that forms the analytic solutions.
Ability to push the boundary of advanced analytics/machine learning/artificial intelligence to the extent of implementing newly proposed algorithms from research papers if necessary.
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