180 Garsfontein Road, Ashlea Gardens, Pretoria
Remote Working
2 days work-from-home in line with Company Policy (only applicable after probation is successfully passed)
Job Purpose
Providing customer centric data analytics, including alternative methods of aggregating raw data (internal and external), which will ultimately influence the way in which we view and act on customer behavior's and customer health, i.e. identifying risks and opportunities; and implementing the use of machine learning to challenge and improve predictive modelling techniques, the available characteristic universe across the customer life cycle and optimizing's segmentation to enhance model performance. Solutions should satisfy customer centricity and digitization objectives.
Minimum Education (essential)
Bachelor's degree in Computer Science, Statistics, Mathematics, Actuarial Science or a related field.
Minimum Education (desirable)
Honors or Masters degree Computer Science, Statistics, Mathematics, or a related field.
Minimum Applicable Experience (years)
5 years
Required Nature Of Experience
- Proven experience in data analysis, statistical modeling, and machine learning.
- Data cleaning, feature engineering, and model validation.
- Data visualization tools (e.g., Tableau, Power BI Qliksense).
- Proficiency in programming languages such as SQL, Python, R.
- Data manipulation and analysis using SQL.
- Big data processing frameworks like Hadoop or Spark.
- Machine learning algorithms and techniques.
- statistical analysis and hypothesis testing.
- Analyzing complex datasets, identify patterns, and extract meaningful insights.
- experimental design and A/B testing methodologies.
- Validate large volumes of data.
- Collect, clean, and preprocess data from various sources.
- Apply statistical techniques and machine learning algorithms to analyze and interpret data.
- Develop predictive models and algorithms to solve complex business problems.
- Perform model validation and fine-tuning to ensure accuracy and reliability.
- Create visualizations and reports to communicate findings effectively.
- Extract actionable insights from data analysis to support decision-making processes.
- Collaborate with business stakeholders to identify key areas for improvement and optimization.
- Provide data-driven recommendations for business strategies and initiatives.
- Conduct ad-hoc analysis to answer specific business questions and solve problems.
- Ensure data quality and integrity throughout the data lifecycle.
- Develop and implement data governance frameworks and best practices.
- Collaborate with data engineers to optimize data pipelines and workflows.
- Stay updated with the latest industry trends and advancements in data science.
- Stay abreast of new techniques, algorithms, and tools in the field of data science.
- Participate in conferences, workshops, and training programs to enhance skills and knowledge.
- Share knowledge and expertise with team members through mentoring and coaching.
