Data Scientist - Assistant Manager

Qualifications and Experience

• 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

• 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