Specialist Data Scientist at Absa Group Limited (Absa)

End Date: March 10, 2026

Job Description

• Participate in design thinking processes tdetermine & confirm hypotheses and priority questions / data challenges & related metrics tbe solved for
• Translate business questions tbe solved intdata requirements & define a data solutions tdeliver against these requirements
• Proactively partner with the data engineering team trefine the data requirements deliver raw data tData Science teams for interpretation & analysis
• Design fit for purpose data interpretation & analysis approaches & create customized data models, algorithms, machine learning tools and recommendation engines tachieve the desired business outcomes
• Use advanced data science skills tmine & 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
• Analyze & interpret complex data sets
• Apply quality assurance frameworks ttest model & analysis techniques (e.g. algorithms, models) & overall data quality
• Apply the testing frameworks tmonitor 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 tmaximize impact & deliver a user friendly product tbusiness
• Contribute tthe consolidation of data solutions intviable 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 tbusiness & show tangible business impact tbe derived from the data science process
• Facilitate peer reviews & feedback on data solutions
• Refine data analysis based on business & peer reviews
• Contribute tthe 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 tadvanced methods for the continuous optimization of data
• Develop and retain models on customer behavior, retention and segmentation
• Build algorithms and design experiments tmerge, manage, interrogate and extract data tsupply tailored reports tcolleagues, customers or the wider organization
• Use machine learning tools and statistical techniques tproduce solutions tproblems
• 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 tuse insights/datasets/code/models across other functions in the organisation 
• Stay curious and enthusiastic about using algorithms tsolve problems and enthuse others tsee 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 tcover specific local issues
• Business process maintenance (segmentation, primacy etc)
• Produce appropriate documentation and training material tenable handover ta 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 tguarantee data quality & data integrity (always) for specific data solutions
• Contribute trisk, 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