Location: ZA, GP, Johannesburg, Baker Street 30
Apply data mining techniques and conduct statistical analysis to large, structured and unstructured data sets to understand and analyse phenomena. 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.
Builds machine learning models from and utilises distributed data processing and analysis methodologies. Competent in Machine Learning programming in R or Python, with supplementary still in Matlab, Java, etc. Familiar with the Hadoop distributed computational platform, including broader ecosystem of tools such as HDFS / Spark / Kafka.Directs the gathering of data for use in Data Science models, ensuring that chosen datasets best reflect the organisations goals. Performs data pre-processing including data manipulation, transformation, normalisation, standardisation, visualisation and derivation of new variables/features. Utilises advanced data analytics and mining techniques to analyse data, assessing data validity and usability; reviews data results to ensure accuracy; and communicates results and insights to stakeholders.Designs various mathematical, statistical, and simulation techniques to typically large and unstructured data sets in order to answer critical business questions and create predictive solutions which drive improvement in business outcomes. Drives analytics and insights across the organisation by developing advanced statistical models and computational algorithms based on business initiatives.Use data profiling and visualisation techniques using tools to understand and explain data characteristics that will inform modelling approaches. 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.Mines data using state-of-the-art methods. Enhances data collection procedures to include information that is relevant for building data models.Codes, tests and maintains scientific models and algorithms; identifies trends, patterns, and discrepancies in data; and determines additional data needed to support insight. Processes, cleanses, and verifies the integrity of data used for analysis. Qualifications Minimum Qualifications, certifications or professional memberships Degree (Information Studies/Information Technology) (Min)Proficiency in application and web development. Structured and Unstructured Query languages e.g. SQL, Qlikview; Tableau; SSIS SSRS, Python JSON , C#, Java, C++, HTML (Preference) Additional Information Experience Required 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.).
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
Adopting Practical Approaches: Adopting practical solutions with an emphasis on learning by doing. This competency requires individuals to utilise common sense when required. Ultimately, this competency is important in order to ensure that organisations implement feasible solutions.
Articulating Information: This competency is about effectively expressing ideas and concerns, giving presentations, explaining things to others as well as showing confidence in the interaction with other people, both strangers and acquaintances alike.
Challenging Ideas: This competency is about an individual facilitating or catalysing change in an organisation. Challenging Ideas emphasises individual behaviours associated with questioning assumptions, challenging established views and arguing personal perspectives.
Data Analysis: Ability to analyse statistics and other data, interpret and evaluate results, and create reports and presentations for use by others.
Data Integrity: The ability to ensure the accuracy and consistency of data for the duration that the data is stored as well as preventing unintentional alterations or loss of data.
Database Administration: Refers to the knowledge and experience required to manage the installation, configuration, upgrade, administration, monitoring and maintenance of physical databases.
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