Data Scientist

Job Description

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.

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

Qualifications

• First Degree, Information Studies/ Statistics/ Data Science
• 3-4 years experience 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.)

Additional Information