Requirements Masters or Doctoral degree or equivalent in Actuarial Science, Finance, Statistics, Mathematics, Financial Mathematics, Business / Financial Risk Management, Informatics, Information Systems, Computer Science, Computer Engineering, etc. Data Science certification (advantageous) Data Analytics / Visualisation certification (advantageous) Project Management certificate (advantageous) Financial Markets Operations certification (advantageous) Financial Risk Management certification (advantageous) Advanced knowledge in building and operating continuous integration and deployment environments Advanced experience with data management and development tools Six to eight years experience in building, maintaining and optimising data and business intelligence (BI) solutions of which at least three to five years should be in building advanced analytics solutions as part of an analytics setup in a company / analytics services provider A minimum of three years experience in deploying, maintaining and optimising advanced analytics solutions Strong understanding of financial instruments, financial risk, operational risk, compliance risk, investment performance measurement and attribution, investment management operations, interest rates, trading operations and financial reporting Experience in a transformational data warehousing and advanced analytics projects (advantageous) Key Performance Areas Work effectively with Business Analysts, Data Analysts and System Analysts and other business units subject matter experts to understand the broader business context and ensure that proposed solutions align with business goals Consult with business to understand business objectives, drivers, functions and structures and develop detailed workflow analyses Consult with business and technical stakeholders to analyst, communicate, document and validate requirements for changes and new solutions Investigate problems and propose possible solutions by interacting with users, and other participating stakeholders Interact with architects, business specialists and business analysts to ensure the solution that will be acquired meets business needs Manage expectations and address concerns or feedback promptly Guide business to acquire solutions Ensure comprehensive documentation of all project activities, findings and decisions Identify value-driving opportunities for the application of advanced analytics in achieving the departmental mandate Execute advanced analytics use cases that derive insights across key departmental subject areas Identify, source and assess relevant structured and unstructured data for statistical modelling Prepare data for statistical modelling by identifying data correlations, and collinearity and perform the required feature engineering Develop robust statistical models that are modular, scalable, deployable, reproducible and versioned for analytics and reporting purposes Ensure that models are validated and meet the required performance thresholds Fully document analytics use cases according to best practice standards (including documentation of model selection, validation, algorithms and code) Monitor, measure and report on analytical results to ensure appropriate business recommendations and insights Deploy, maintain, enhance, optimise and automate analytical solutions Identify and mitigate risks (i.e model biases) and ensure the adherence to ethics principles (fairness, privacy, transparency and accountability) with respect to data and advanced analytics modelling Align work outputs to the relevant organisational strategies as well as Enterprise Information Management (EIM) and governance standards and frameworks Communicate complex analytics concepts clearly and concisely to assist researchers and stakeholders in interpreting model outputs and informing decision and policy actions Collaborate and proactively engagement stakeholders from business and functional support areas across the analytics life cycle to ensure solutions are well formulated, deployed, supported and adopted Keep abreast of industry best practices, techniques and technologies and lead the implementation thereof to ensure the creation of value-enhancing advanced analytics solutions Key Deliverables 1. AI / ML Maturity Develop AI / ML Maturity Assessment Tool Facilitate the AI / ML Maturity Assessment Develop and obtain approval for the AI / ML Assessment Report 2. Data Science Competency Model 3. AI / ML DevOps Functional Specify and lead the deployment of the AI / ML DevOps sandbox environment Specify the DataOps requirements with alignment to the departmental Data Strategy
