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Assist in applying data mining techniques and conduct statistical analysis to large, structured and unstructured data sets to understand and analyse phenomena. Model 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.
- Assist the gathering of data for use in Data Science models, ensuring that chosen datasets best reflect the organisations goals.
- Perform data pre-processing including data manipulation, transformation, normalisation, standardisation, visualisation and derivation of new variables/features.
- Utilise 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.
- Assists in building 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.
- Assist 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.
- Assists analytics and insights across the organisation by developing advanced statistical models and computational algorithms based on
business initiatives, codes, tests and maintains scientific models and algorithms and 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.
- Liaise and collaborate with the Data Science Guild providing support to stakeholders in the department for its data centric needs.
- 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.
- Supports and implements operational IA plan, rules, methodologies and coding initiatives in order to ensure IA for remediation efforts.
- Support and implements the strategy for productionalising automation software so that it is accurate and well maintained.
- Supports business integration through integrating model outputs into end-point production systems, incorporating business requirements and knowledge of best practices.
- Supports various mathematical, statistical, and simulation techniques to answer business questions within specific areas of focus.
- Develops modelling solutions that enable the forecast of quality data outcomes.
- Ensures that volumetric predictions are modelled so that resource requirements are optimally considered.
- Supporting reporting production ensuring sustainable and effective modelling solutions.
- 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 respective stakeholders, presenting trends, correlations and patterns found in complicated datasets in a manner that clearly and concisely conveys meaningful insights and defend
recommendations under the supervision of data scientists.
- Utilise the appropriate data storage and data mining tools to ensure value can be extracted from the sourced data.
- Mines data using state-of-the-art methods and enhances data collection procedures to include information that is relevant for building models.
Type of Qualification: First Degree
- Field of Study: Information Studies
- Type of Qualification: First Degree
- Field of Study: Information Technology
Data & Analytics
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.
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. Experience in building models (credit scoring, propensity models, churn, etc.)
- Adopting Practical Approaches
- Articulating Information
- Challenging Ideas
- Checking Details
- Examining Information
- Exploring Possibilities
- Interacting with People
- Interpreting Data
- Meeting Timescales
- Producing Output
- Providing Insights
- Team Working
- Data Analysis
- Data Integrity
- Database Administration
- Knowledge Classification
- Research & Information Gathering