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Reporting to Head Data Science, the Data Scientist will 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 internal and external stakeholders, data, technology and support teams to turn data into critical information used to make sound business decisions. Execute intelligent automation and predictive modelling.
Job Responsibilities/ Accountabilities:
- Support in 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.
- Document business requirements
- Develop model documentation for the purpose of model validation
- Develop dashboards and presentations for business insights using tools like PowerBI and Microsoft power point
- 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.
- 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.
- Create, maintain and optimise modelling solutions that enable the forecast of quality data outcomes. Ensures that volumetric predictions are modelled so that resource requirements are optimally considered. Develops and maintains optimal evaluation techniques to ensure that modelled outcomes are rigorous and creates model performance tracking. Drives sustainable and effective modelling solutions.
- Provide input into Data management and modelling infrastructure requirements and adheres to the organisations’s infrastructure development processes, including the management of User Acceptance Testing (UAT). Conducts regression testing across all relevant systems as required.
- Build 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 Java, etc. Familiar with the Hadoop distributed computational platform, including broader ecosystem of tools such as HDFS / Spark / Kafka
- Degree in Statistics, Machine Learning, Mathematics, Computer Science, Economics, or any other related quantitative field.
- Working experience in the finance industry via direct employment or consultancy
- 3-5 years’ experience in working with structured and 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. Experience with data visualisation tools, such as Power BI, Tableau, etc.
- Proficiency in application and web development. Structured and Unstructured Query languages e.g. SQL, Power BI; Qlikview; Tableau; SSIS SSRS, R, Python, JSON , C#, Java, C++, HTML
- Experience with the use of GIT
- 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.).
- A suitable candidate will also have had experience working with and influencing and possess vast experience and expertise with probability and statistics, inclusive of machine learning, experimental design, and optimization. As a bonus he will also have had experience working with Hadoop.
- Communication Skills: Communication skills will also be a necessity for the Data Scientist. He must be able to convey important messages and information
- Ms Office/Software: Outstanding skills in the use of Ms Word, Ms Excel, PowerPoint, and Outlook, which will all be necessary for the creation of both visually and verbally engaging reports and presentations, for senior data science management, executives, and stakeholders.
- The candidate must also demonstrate exceptionally good skills in SQL server reporting services, analysis services, PowerBI, integration services, Salesforce, or any other data visualization tools.
- Technological Savvy/Analytical Skills: Technologically adept and especially demonstrate an understanding of database and computer software.
- Interpersonal Skills: A suitable candidate for this position will be a team-collaborator, be result-oriented, be proactive and self-driven requiring minimal supervision, be open and welcoming to change, be a creative and strategic thinker, have innovative problem-solving skills, be highly organized, have an ability to handle multiple simultaneous tasks prioritize and meet tight deadlines, and demonstrate calmness in times of uncertainty and stress.
- People Skills: A people person who is able to form strong, lasting, and meaningful bonds with others people. This will make him/her an approachable and trustworthy individual who junior personnel readily follow and who Data and Analytics colleagues and stakeholders trust and who’s insights they give credit to, making execution of his duties that much easier.