The purpose of this job is to utilise data, machine learning, statistical and mathematical models to solve business problems and enabling the effective use of data by utilising cutting-edge technologies.
Responsibilities:
Utilise data, machine learning, statistical and mathematical models to address the organisations business problems
- Provide into the database architecture design, maintenance and optimisation
- Identify areas to increase efficiency and automation of processes
- Contribute to create automated processes to extract, transform and load data
- Build predictive models and machine-learning algorithms.
- Deploy & maintain ML models as APIs for use in live environments
- Combine models through ensemble modelling
- Enhance, find patterns in and build models on large data sets using the appropriate modelling technique and analysis methodologies.
- Apply data mining techniques and perform statistical analysis on large data sets.
- Develop experimental design approaches to validate findings or test hypotheses.
- Perform analysis, interpret and explain results using appropriate statistical tools and techniques translating findings into clear, actionable and timely insights.
- Validate analysis using appropriate techniques i.e. applying test data sets, A/B testing, scenario modelling, etc.
- Productionise models using the company’s standard processes and techniques.
- Monitor the predicted outcomes of models and understand business requirements to ensure that models are delivered in an appropriate format.
- Implement and recalibrate models to enhance and improve prediction and improvement of models to ensure alignment
- Implementation of preventive controls to minimise model risk
- Develop data science products and solutions for the organisation
- Translate meta data into explanatory report and visuals for easy understanding to end user.
- Present the findings and proposals to business users in an understandable format
- Drive continuous improvement of operational reporting processes and policies in support of business objectives and growth.
Undertake and source data collection, pre-processing and analysis for the organisation
- Identify new diverse data sources and automate data sourcing processes with consideration to partner with third party sources.
- Undertake pre-processing of structured and unstructured data.
- Analyse large amounts of information to discover trends and patterns.
- Ensure that the data science and analytics strategy is implemented and that process improvements for data science and analytics activities are identified and actioned.
- Collaborate with the business owners to identify challenges and opportunities, requirements and expected outcomes of modelling to ensure strategic alignment and value creation.
- Collaborate with subject matter experts and data analytics team to select the relevant sources of data and information.
- Partner with the Data Engineering team as necessary to manage data ingestion.
Present embedded analytics in data models to enable quick visualization
Ensure data governance and data quality assurance standards are upheld
Requirements
- Actuarial Science Qualification
- At least 5+ years’ experience as a data analyst with SAS and or Python experience. Knowledge of the regulatory analytics domain within banking/insurance sector will be an advantage. Firm grip on analytics tools like SQL, R and Python.
- Working knowledge or experience in the fields of data management, operations analytics, big data and artificial intelligence is preferred.
Desired Skills:
- Data Science
- data analyst
- SAS
- Python
- SQL
- Data management
- Analytics
Desired Work Experience:
- 5 to 10 years
Desired Qualification Level:
- Degree