Job Summary

• Leverage deep data science expertise in advanced statistics, data wrangling, data mining, data analysis, feature engineering &predictive modeling, storytelling, distributed computing & data visualisation, machine learning tools & data intuition to define, build, operationalize & continuously improve data solutions that deliver relevant, quality assured, accurate & commercially impactful data to the business.

Job Description

• Participate in design thinking processes to determine & confirm hypotheses and priority questions / data challenges & related metrics to be solved for
• Translate business questions to be solved into data requirements & define a data solutions to deliver against these requirements
• Proactively partner with the data engineering team to refine the data requirements deliver raw data to Data Science teams for interpretation & analysis
• Design fit for purpose data interpretation & analysis approaches & create customized data models, algorithms, machine learning tools and recommendation engines to achieve the desired business outcomes
• Use advanced data science skills to mine & interpret data. These include but are not limited to: advanced statistics, data wrangling, data mining, data analysis, feature engineering & predictive modeling, distributed computing, machine learning tools & data intuition
• Analyse & interpret complex data sets
• Apply quality assurance frameworks to test model & analysis techniques (e.g. algorithms, models) & overall data quality
• Apply the testing frameworks to monitor and analyse model performance & data integrity on all data assignments
• Produce business insights and recommendations based on data analysis & modeling concluded & where relevant with knowledge and experience of e.g. Databricks, Python, Hadoop, Apache Spark
• Use storytelling and data visualization techniques to maximize impact & deliver a user friendly product to business
• Contribute to the consolidation of data solutions into viable end products (in the language of business) that can be leveraged on an ongoing basis e.g. dashboards, reports etc.
• Present data analysis (trends, insights, forecasts) & findings to business & show tangible business impact to be derived from the data science process
• Facilitate peer reviews & feedback on data solutions
• Refine data analysis based on business & peer reviews
• Contribute to the assessments of the effectiveness and accuracy of new data sources & data gathering techniques
• Promote data literacy with your business stakeholders by sharing best practices and showing tangible business impact & recommendations as a direct result of the the data solutions provided
• Stay ahead of the curve on data science trends & leading practice data science tools and techniques & transition the organisation to advanced methods for the continuous optimization of data
• Develop and retain models on customer behavior, retention and segmentation
• Build algorithms and design experiments to merge, manage, interrogate and extract data to supply tailored reports to colleagues, customers or the wider organization
• Use machine learning tools and statistical techniques to produce solutions to problems
• Assess the effectiveness of data sources and data-gathering techniques and improve data collection methods
• Conduct research from which you’ll develop prototypes and proof of concepts
• Look for opportunities to use insights/datasets/code/models across other functions in the organisation
• Stay curious and enthusiastic about using algorithms to solve problems and enthuse others to see the benefit of your work.
• Development of standardised modelling of behaviour/retention/value in customers
• Maintenance of dictionaries: codes, dimensions, setting up tables etc
• Run and compare models on a regular basis
• Run Cluster specific scenarios to cover specific local issues
• Business process maintenance (segmentation, primacy etc)
• Produce appropriate documentation and training material to enable handover to a BAU team.
• KVD Predictions
• Using and deriving value from implemented models

Accountability: Risk & Governance

• Identify data risks and mitigate these (pre, during & post solution deployment / data delivery)
• Create business cases & solution specifications for various governance processes (if required)
• Apply data quality assurance frameworks and tools to guarantee data quality & data integrity (always) for specific data solutions
• Contribute to risk, governance, compliance & broader regulatory processes as a data science expert (if & when required)

 Accountability: People

• Coach & mentor other data scientists
• Conduct peer reviews, testing, problem solving within and across the broader team

The following degrees and/or  subjects may be particularly useful:

• Data science
• Industrial engineering
• Mathematics
• Statistics
• Actuarial science
• Business mathematics and informatics

Additional Job Description

• Ensure that all activities and duties are carried out in full compliance with regulatory requirements, Enterprise Wide Risk Management Framework and Absa Policies and Policy Standards.
• Understand and manage risks and risk events (incidents) relevant to the role

Education

• Bachelor’s Degree: Information Technology

End Date: September 23, 2025