Technical à Afrique du Sud

Empowering Africa’s tomorrow, together…one story at a time.

With over 100 years of rich history and strongly positioned as a local bank with regional and international expertise, a career with our family offers the opportunity to be part of this exciting growth journey, to reset our future and shape our destiny as a proudly African group.

The Data Science Lead will be required to deliver the predictive models, lead the engineering discussions to implement the models as well as design/maintain the visualisation to track fraud losses and rule & model performance.

The successful candidate must have proven (five years plus) experience of implementing end-to-end machine learning solutions in production environments with strong proficiency in Python, SQL and Machine Learning frameworks.

The candidate will also have proven track record of leading data science teams and manage diverse business stakeholders.

Essential qualifications : Master's degree in Computer Science, Data Science, Statistics or Mathematics.

This role will be responsible for improving the current data science capability to support the future of fraud prevention with advanced analytical models and machine learning systems. The role will be responsible to define a detailed data exploration process with analytical methods that supports feature engineering, model deployment, governance, data pattern solutions that's predictive and visualisation. The role will work closely with the Fraud Risk team and the real-time monitoring squads. The role will also be expected to lead a technical team consisting of data science-, model governance-, data engineering-, visualisation-and-reporting skills. Good communication-and-relationship building skills are required to support the successful delivery of complex problems in business.

• Develop a deep understanding of the business area and associated challenges.
• Cascade business challenges & commercial understanding to the data science team working on data solutions.
• Further translate the business challenges into key questions that can be solved with data solutions & coach other data scientists to do the same.
• Lead design thinking processes & leverage own deep design thinking skills to determine & confirm hypotheses and priority questions / data challenges & related metrics to be solved for.
• Translate vague questions into specific & more tangible data analysis.
• Translate this business understanding into data requirements & define a data strategy to deliver against these requirements.
• Proactively partner with the data engineering team to refine the data requirements and develop a technical roadmap to 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.
• Leverage the above to analyse & interpret complex data sets & manage large data volumes.
• Develop data quality assurance frameworks and tools 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.
• Translate analysis (make inferences & reach conclusions) into commercially relevant business insights & leverage storytelling and data visualization techniques to maximize impact & deliver a user friendly product to business.
• Consolidate data solutions into viable end products (in the language of business) that can be leveraged on an ongoing basis e.g. dashboards, reports etc.
• Workshop data analysis (trends, insights, forecasts) & findings with business & show tangible business impact to be derived from the data science process.
• Regularly refine data analysis based on business inputs leading to the finalization of the data solution.
• Provide tangible, practical & commercially viable recommendations to the business based on the outcomes of the analysis & influence business decision making (even if it at times means delivering difficult messages).
• Proactively partner the data engineering teams to assess the effectiveness and accuracy of new data sources & data gathering techniques.
• Positively contribute to the data architecture direction by providing expertise on data science tools, techniques and the broader business data requirements.
• Promote data literacy across the enterprise by sharing best practices and showing tangible business impact & recommendations as a direct result of the the data solutions provided.
• Proactively 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.

• Identify data risks and mitigate these (pre, during & post solution deployment / data delivery).
• Create business cases & solution specifications for various governance processes (if required).
• Create knowledge & document management processes and practices for data management aligned to Group Risk, Governance & Compliance & Broader Regulatory requirements.
• Apply data quality assurance frameworks and tools to guarantee data quality & data integrity (always) across the business area.
• Provide risk, governance, compliance & broader regulatory reporting as required.
• Contribute to risk, governance, compliance & broader regulatory processes as a data science expert (if & when required).
• Deliver on time & on budget (always).

• Coach & mentor other data scientists.
• Conduct peer reviews, testing, problem solving within and across the broader team.
• Participate as a subject matter expert in the development & development planning of the data science team as required.

Bachelor's Degree: Information Technology.

Absa Bank Limited is an equal opportunity, affirmative action employer. In compliance with the Employment Equity Act 55 of 1998, preference will be given to suitable candidates from designated groups whose appointments will contribute towards achievement of equitable demographic representation of our workforce profile and add to the diversity of the Bank.

Absa Bank Limited reserves the right not to make an appointment to the post as advertised.