RequirementsMasters 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 environmentsAdvanced experience with data management and development toolsSix 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 providerA minimum of three years experience in deploying, maintaining and optimising advanced analytics solutionsStrong understanding of financial instruments, financial risk, operational risk, compliance risk, investment performance measurement and attribution, investment management operations, interest rates, trading operations and financial reportingExperience in a transformational data warehousing and advanced analytics projects (advantageous)Key Performance AreasWork 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 goalsConsult with business to understand business objectives, drivers, functions and structures and develop detailed workflow analysesConsult with business and technical stakeholders to analyst, communicate, document and validate requirements for changes and new solutionsInvestigate problems and propose possible solutions by interacting with users, and other participating stakeholdersInteract with architects, business specialists and business analysts to ensure the solution that will be acquired meets business needsManage expectations and address concerns or feedback promptlyGuide business to acquire solutionsEnsure comprehensive documentation of all project activities, findings and decisionsIdentify value-driving opportunities for the application of advanced analytics in achieving the departmental mandateExecute advanced analytics use cases that derive insights across key departmental subject areasIdentify, source and assess relevant structured and unstructured data for statistical modellingPrepare data for statistical modelling by identifying data correlations, and collinearity and perform the required feature engineeringDevelop robust statistical models that are modular, scalable, deployable, reproducible and versioned for analytics and reporting purposesEnsure that models are validated and meet the required performance thresholdsFully 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 insightsDeploy, maintain, enhance, optimise and automate analytical solutionsIdentify 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 modellingAlign work outputs to the relevant organisational strategies as well as Enterprise Information Management (EIM) and governance standards and frameworksCommunicate complex analytics concepts clearly and concisely to assist researchers and stakeholders in interpreting model outputs and informing decision and policy actionsCollaborate and proactively engagement stakeholders from business and functional support areas across the analytics life cycle to ensure solutions are well formulated, deployed, supported and adoptedKeep abreast of industry best practices, techniques and technologies and lead the implementation thereof to ensure the creation of value-enhancing advanced analytics solutionsKey Deliverables1. AI / ML MaturityDevelop AI / ML Maturity Assessment ToolFacilitate the AI / ML Maturity AssessmentDevelop and obtain approval for the AI / ML Assessment Report2. Data Science Competency Model3. AI / ML DevOps FunctionalSpecify and lead the deployment of the AI / ML DevOps sandbox environmentSpecify the DataOps requirements with alignment to the departmental Data Strategy