Graduate Programme at Alexander Fobers for Data Science, Computer Science, Statistics graduates

Designation: Graduate Programme: AI & Robotic Process Automation

Category: Group Technology - OF5308

Posted by: Alexander Forbes

Posted on: 29 Jul 2025

Closing date: 12 Aug 2025

Location: Sandton

Purpose of the Job:

Overview:

Graduate Programme Area: Information Management

Sub Area of Graduate Programme: AI and RPA (data) management

• The graduate candidate we are looking for will be:

o a highly motivated and analytical individual with a strong academic background (backed up with hands on technical lab work) in data science/ data quality and data management, machine learning, and experience in applying artificial intelligence computing methods

• He or she will be proficient in programming languages such as:

o Python, Fedora-Linux, Windows server, R, MS SQL, PowerShell scripting PowerBI, data cloud platforms inter alia Amazon AWS, MS Azure, Google, Snowflake with hands-on experience performing data analysis, predictive modelling and data storytelling supported by data visualization methods

• He or she should be comfortable to interact with:

o big data sets (databases) where the typical database table records will reference at least a million or more individual data entries.

• He or she should be comfortable to interact with:

o APIs – basic configuration and debugging.

• Ideal candidate personalities will be individuals who:

o likes to ask questions, are technical inclined, hands-on, enjoy working in small teams, are fond of problem solving and self-study to remain current with the ever-developing world of data science and AI technical landscape(s), displays confidence, self-awareness and the ability to work independently.

• Successful candidates:

o will work either remotely or onsite at our Sandton office.

o Display a deliverable orientation (outcome focused) – meaning, their nature should be inclined to express an intent to get things done!

Requirements:

• A national Diploma or Degree(s) majoring in Data Science, Computer Science, Statistics, or related fields

• Additionally, the individual should ideally also be working towards (or alternatively already obtained) multiple certifications in the use of SQL, data science, AI, or AI applications using data labs.

• Prior exposure with the ability to conduct AI model prompt training (enhancements) and or model-local hosting methods e.g. Ollama, LM studio, MS Co-Pilot studio or similar (will be advantageous)

• Tertiary Institution(s): all are welcome to apply

• Graduation Year(s): three (3) or more

• Relevant coursework should include data quality (DQ) management principles, setup and monitoring of data pipelines, using machine learning (ML) methos, MS or MYSQL, interpreting data modelling outcomes to select reliable data training scenarios, perform data mining and profiling, conduct statistical analysis with data storytelling, AI model selection, re-enforcement learning, prompting and design. Selection and or configuration of AI virtual agents and patterns – select, design, operate, monitor and support.

• Tools: MS Teams, Python, Kaggle, Linux, Jupyter IDE, Git, Excel. SQL. Postman, data handling and manipulation utilities

Main Accountabilities:

• Participate in data collection and preprocessing for AI projects

• Develop and test machine learning models under supervision

• Analyze datasets to extract actionable insights for business problems

• Present results to technical and non-technical stakeholders

• Contribute to documentation and knowledge sharing within the team

Key Competencies:

Technical Skills

• Programming Proficiency: Emphasize strong skills in Python, as it is the industry standard for AI, along with familiarity in R or Java. These languages are essential for building algorithms, automating data tasks, and working with AI frameworks

• Machine Learning & Deep Learning: Highlight foundational knowledge in machine learning algorithms, deep learning models, and neural networks. Understanding supervised and unsupervised learning, as well as practical experience with model training and evaluation

• Data Literacy & Analytics: Ability to collect, clean, preprocess, and analyze data is crucial. Familiarity with tools such as pandas, NumPy, SQL, and data visualization libraries (e.g. Matplotlib, Seaborn, Tableau) is important for communicating insights and supporting model development

• Mathematics and Statistics: A solid grasp of linear algebra, calculus, probability, and statistics is foundational for understanding and building AI models

• Familiarity with AI Tools & Frameworks: Experience with data management, data virtualisation, machine learning libraries s), cloud platforms and big data tools

Soft Skills

• Problem-Solving & Critical Thinking: AI projects often involve complex, ambiguous problems. Emphasize your ability to troubleshoot, analyse challenges, and develop creative solutions

• Collaboration & Communication: AI teams are multidisciplinary. Highlight experience working in diverse teams, communicating technical concepts to non-technical audiences, and contributing to cross-functional projects

• Adaptability & Continuous Learning: The AI field evolves rapidly. Show willingness to learn new tools, adapt to new challenges, and stay updated with the latest advancements

• Ethics & Governance Awareness: Understanding AI ethics, bias mitigation, data privacy, and regulatory compliance is increasingly important. Awareness of responsible AI practices and explainability

• Strong communication and presentation skills

• Team collaboration

• Time management

Click HERE to APPLY