Role Responsibility

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 analyze phenomena. Model complex business problems, discovering insights and opportunities through statistical, algorithmic, machine learning and visualization 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 modeling.

Responsibilities of the Data Scientist

• Support in the gathering of data for use in Data Science models, ensuring that chosen datasets best reflect the organization's goals.
• Perform data pre-processing including data manipulation, transformation, normalization, standardization, visualization, 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 Power BI and Microsoft power point
• Utilize advanced data analytics and mining techniques to analyze data, assess data validity and usability; reviews data results to ensure accuracy; and communicate results and insights to stakeholders.
• Use data profiling and visualization 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.
• Develops and maintains optimal evaluation techniques to ensure that modelled outcomes are rigorous and creates model performance tracking.
• Competent in Machine Learning programming in Python, with supplementary still in Java, etc. Familiar with the Hadoop distributed computational platform, including broader ecosystem of tools such as HDFS / Spark / Kafka

• Support in the gathering of data for use in Data Science models, ensuring that chosen datasets best reflect the organization's goals.
• Perform data pre-processing including data manipulation, transformation, normalization, standardization, visualization, 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 Power BI and Microsoft power point
• Utilize advanced data analytics and mining techniques to analyze data, assess data validity and usability; reviews data results to ensure accuracy; and communicate results and insights to stakeholders.
• Use data profiling and visualization 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.
• Develops and maintains optimal evaluation techniques to ensure that modelled outcomes are rigorous and creates model performance tracking.
• Competent in Machine Learning programming in 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 is preferred
• 1-3 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 visualization 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, R, Python, JSON, Java, HTML
• Experience with the use of GIT