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Snr Data Scientist ( JHB and Cape Town )

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Job Type
Employer Type
Date Added 10 May 2022
Expiry Date 7 Jun 2022
* There has been 1 application to this job.
* This job has been viewed 2790 times.
Capitec Bank

All Areas

Market related


Role details:

• To assist in building and delivering the AI strategy to ensure Capitec is able to compete in a fast changing landscape. Data science will be a key strategic differentiator in the future.
• Data scientists will help automate and improve processes, create new products and services and assist with improved decision making based on data.
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The role of a Data Scientist involves generic technical tasks and responsibilities. There are distinct levels within the job which are defined by experience, skills, autonomy and the level at which the incumbent operates and contributes. Four levels have been identified for the function: Level 1, Level 2, Level 3 and Level 4. Senior Data Scientists fulfil the level 5 role and are covered by a separate job profile.

NB. The skill sets and role definitions in data science as a whole are still evolving in industry and experienced data scientists (levels 3-5) are commonly referred to as Machine Learning Developers, Machine Learning Engineers, AI Researchers and AI Developers.

7.1 Ownership and responsibility of delivery
• Responsible for adhering to formal data science process and ensuring best practices are applied in each project.
• Responsible for being knowledgeable of the relevant environment in which they will work;
o Display interest in understanding the allocated client area: ask relevant questions
o Responsible for effectively and efficiently addressing client queries / questions
o Ask relevant questions to ensure understanding of the core business need driving the specific request
• Review and check integrity of existing ML models or analysis affecting within business area and conduct maintenance to ensure relevance.
• Follow-up with relevant IT departments with regards to issues relating to supporting applications and technologies used in Data Science.
• Escalate serious issues to Lead or Guild Manager: Data Science where required
• Interact with business users on a business level to understand the potential risk / business impact of need.
7.2 Documentation and prioritisation of business requirement
• Accurately document the business requirement using the formal Data Science ideation templates
o This includes amongst others, business requirements gathering, solution design, model experimental design and validation, SDLC process and QA checks as well as monitoring when going to production.
• Documentation needs to adhere to data science life cycle and meet formal regulatory and internal risk and compliance standards (MTSC).
• Translate business needs into technical/system requirements applicable to IT and various other technically focused departments to ensure machine learning models can be implemented
• Complete solution design within required context, e.g. delivering models as micro services
• Differentiate and prioritise work from clients according to value that it will add on a company level.
• Communicate project priorities to clients and team members to:
? Ensure effective management of client expectations, and
? Ensure the accurate allocation of the team’s capacity.
• Prepare formal communication / feedback to stakeholders on developed reports, analysis or models in an understandable format

7.3 Build machine learning models and AI solutions
• Develop most appropriate machine learning model based on specific business need.
• Design / develop and implement machine learning models or solutions within business
• Monitor continuous relevance, predictive strength and stability within business
• Conduct development in line with prescribed data science lifecycle of Capitec
• Extend knowledge base and development of new statistical/machine learning methods to improve business positioning within machine learning and AI
• Assist in delivering the AI strategy and business objectives
• Identify opportunities where new techniques and tools can be used to disrupt Capitec from within

7.4 Interaction & engagement with Team
• Adhere to principles of data science team to ideate and build supportive and a collaborative culture.
• Adhere to agreed housekeeping principles on internal folders, databases, models and deployment thereof.
• Regularly review & clean-up own work
• Adhere to Data Science life cycle and best practices to ensure continuity in all tasks.
• Assist with the establishment of processes to ensure effective resolution of relevant problem statements
• Share knowledge and learnings with team on a regular basis
o Test / share ideas with team members to ensure knowledge sharing.
• Internal and external code reviews and models evaluation
o Collaborate and assist with machine learning model design sessions to insure best practices are adhered to.
o Actively participate in activities to learn optimal coding skills, perform code reviews, check accuracy and ensure consistent work quality.
o Take and accept final ownership for the building of code, variables and models.
• Responsible for ensuring a detailed and effective hand-over process before planned periods of absence (i.e. taking leave).
o Proactively arrange with a specific team member to drive / take ownership of tasks that need to be completed during own absence.
o Remain responsible for the successful delivery of tasks at agreed standards and deadlines during own absence.

7.5 Research and Continuous development
• Show conscious effort towards self-improvement and development by gaining:
o An understanding of the different business areas (i.e. Marketing, BSC and Operations) and what is being done over the world to disrupt and use AI within the area.
o Knowledge of all products and functions within the business.
• Show an eagerness to learn / gain required and additional skills specifically related to AI and ML
• Keep up to date with latest advances in field and share with team to ensure strong AI competency is being built.
• Contribute to open source projects where relevant or academic research


- 5+ years of experience

Proven experience in:
• 2+ years of experience in building machine learning models in Python/R
• Business analysis and requirements gathering
• Reproducible coding experience and working with source control tools e.g. Git, Bitbucket
• Experience in deploying models into production
• Spark, Hadoop or similar big data coding experience
• Working in remote environments, e.g. Docker, Linux
• Working in cloud environments, e.g. Azure, AWS