Data Scientist

at Standard Bank
Location Johannesburg, South Africa
Date Posted Sep 15, 2020
Category IT Jobs
Job Type Full-time


  • Oversee 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. Oversee predictive modelling.


    Key Responsibilities/Accountabilities


     Oversees business integration through integrating model outputs into end-point production
    systems, where requirements must be understood and adopted relating to data collection,
    integration and retention requirements incorporating business requirements and knowledge
    of best practices.

     Oversees the gathering of data for use in Data Science models, ensuring that chosen
    datasets best reflect the organisations goals. Oversees 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 result to ensure accuracy, communicates
    results and insights to executive leadership.
     Guides and validates the design of various complex mathematical, statistical, and simulation
    techniques to answer critical business questions and create predictive solutions which drive
    improvement in business outcomes. Drives analytics and insights within required business
    unit by developing advanced statistical models and computational algorithms based on
    business initiatives.
     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.
     Presents results and recommendations to executive leadership and influences future
    business plans based on insights using excellent communication, presentation and
    visualization capabilities.
     Supervises and oversees the mining of data using state-of-the-art value extraction methods.
    Enhances data collection procedures to include information that is relevant for building data

     Liaise and collaborate with the entire Enterprise Data Office, providing support to the entire
    department for its data science needs. Collaborate with subject matter experts to select the
    relevant sources of information and translates the business requirements into data
    mining/science outcomes. Supports an executive leadership by identifying and applying best
    practices in field of advanced analytics (statistics, operations research, etc.) across the
     Acts as a subject matter expert from a data science perspective and provides input into all
    decisions relating to data science and the use thereof. Educate the organisation on data
    science perspectives on new approaches, such as testing hypotheses and statistical
    validation of results. Validates and certifies the work of other data scientists and trains team
    members in statistical models and guides junior colleagues or less experienced staff on
    projects and drives leading practice.

     Develops, implements, monitors and maintains a comprehensive operational IA plan, rules,
    methodologies and coding initiatives in order to drive IA for remediation efforts. Develops and
    co-ordinates a comprehensive strategy for productionalising automation software so that it is
    accurate and well maintained.

    Risk, Regulatory, Prudential & Compliance
     Guides the data management and modelling infrastructure requirements and monitors the
    implementation thereof through the organisations infrastructure development processes,
    adherence to the organisations model production processes, including UAT. Ensures
    adherence to governance processes to manage the ongoing enhancement and maintenance
    of business rules. Conducts regression testing across all relevant systems as required.

     Overseeing activities of the junior team members, ensuring proper execution of their duties
    and alignment with the organisations vision and objectives. Provide oversights and expertise
    to the Data Science team. Required to draw performance reports and strategic proposals
    form his gathered knowledge and analyses results for senior executive leadership.
    Technology & Architecture
     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.


    Preferred Qualification and Experience

    Post Graduate Diploma - Information Studies
    Post Graduate Degree - Information Technology
    Proficiency in application and web development. Structured and Unstructured Query languages e.g. SQL, Teradata, Qlikview; Tableau, Python, C#, Java, C++, HTML
    8-10 years - Proven development experience in software / software engineering.  Up to date with developments in IAfield.  Experience in technical business intelligence; in depth understanding of the banks data processes, systems and products. Knowledge of IT infrastructure and data principles forming the basis for data quality management. Project management experience. Exposure to data governance and regulatory matters. Experience in building models (credit scoring, propensity models, churn, etc
    8-10 years - Experience in working with unstructured data (e.g. Streams, images)Understanding of dataflows, data architecture, ETL andprocessing of structured and unstructured data.Using data mining to discover new patterns from large dataseImplement standardand proprietary algorithms for handling and processing data. Experience with common data science toolkits, such as SAS, R, SPSS, etc.with data visualisation tools, such as Power BI, Tableau, etc.

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