Data Scientist at Stanbic Bank

Job Description

Client

• Supports business integration through integrating model outputs into end-point production systems, incorporating business requirements and knowledge of best practices.

Data

• Assist 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.
• Assists 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.
• 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.
• Utilises 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.

People

• 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.

Product

• 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.
• 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.

Technology & Architecture

• 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.

Qualifications

• First Degree in Information Studies and or Information Technology

Preferred Qualifications

• Post Graduate Degree in Information Studies
• Other Minimum Qualifications, certifications or professional memberships
• Proficiency in application and web development. Structured and Unstructured Query languages e.g. SQL, Qlikview; Tableau, Python, C#, Java, C++, HTML
• Completion of online coursework in Data Science through Udemy, Coursera, Udacity, etc.

Experience Required

• 3-4 years in 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.)
• 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.