Model complex business problems, discovering insights and opportunities through statistical, algorithmic, machine learning and visualisation techniques, working closely with clients, data and technology teams to turn data into critical information used to make sound business decisions.
Execute intelligent automation and predictive modelling. Key Responsibilities / Accountabilities Technology & Architecture
Familiar with the Hadoop distributed computational platform, including broader ecosystem of tools such as HDFS / Spark / Kafka.
Risk, Regulatory, Prudential & Compliance
Conducts regression testing across all relevant systems as required. Client
Performs data pre-processing including data manipulation, transformation, normalisation, standardisation, visualisation and derivation of new variables / features.
and communicates results and insights to stakeholders.
Drives analytics and insights across the organisation by developing advanced statistical models and computational algorithms based on business initiatives.
Communicate data information to business with various skill levels and in various roles, presenting trends, correlations and patterns found in complicated datasets in a manner that clearly and concisely conveys meaningful insights and defend recommendations.
Processes, cleanses, and verifies the integrity of data used for analysis. People
Collaborate with subject matter experts to select the relevant sources of information and translates the business requirements into data mining / science outcomes.
Presents findings and observations to team for development of recommendations.
Educate the organisation on data science perspectives on new approaches, such as testing hypotheses and statistical validation of results.
Ensure ongoing knowledge of industry standards as well as best practice and identify gaps between these definitions / data elements and organisation data elements / definitions. Product
Develops and maintains optimal evaluation techniques to ensure that modelled outcomes are rigorous and creates model performance tracking.
Drives sustainable and effective modelling solutions.
Develops and co-ordinates a comprehensive strategy for productionalising automation software so that it is accurate and well maintained.
Preferred Qualification and Experience Minimum Qualification : Information Studies Preferred Qualification : Information Technology Other Minimum Qualifications, certifications or professional memberships Proficiency in application and web development.
Structured and Unstructured Query languages e.g. SQL, Qlikview; Tableau; SSIS SSRS, Python JSON , C#, Java, C++, HTML Knowledge / Technical Skills / Expertise Technology Business Partnering 5-7 years Proven development experience in software and software engineering.
Understanding of financial services data processes, systems, and products. Experience in technical business intelligence.
Knowledge of IT infrastructure and data principles. Project management experience. Exposure to governance and regulatory matters as it relates to data.
Experience in building models (credit scoring, propensity models, churn, etc.). Data Monetisation 5-7 years Experience in working with unstructured data (e.
g. Streams, images) Understanding of data flows, data architecture, ETL and processing of structured and unstructured data.
Using data mining to discover new patterns from large datasets. Implement standard and proprietary algorithms for handling and processing data.
Experience with common data science toolkits, such as SAS, R, SPSS, etc. Experience with data visualisation tools, such as Power BI, Tableau, etc.