AI & RPA - EvdB

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

Comfortable to interact with:

• Big data sets (databases) where the typical database table records will reference at least a million or more individual data entries.
• APIs basic configuration and debugging.
• Ideal candidate personalities will be individuals who:
• 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: will work either remotely or onsite at our Sandton office.
• Display a deliverable orientation (outcome focused) meaning their nature should be inclined to express an intent to get things done!

Requirements:

• 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)
• 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

Proficient in programming languages such as:

• 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

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

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