Data Scientist

Introduction: DataProphet is a global leader in Artificial Intelligence (AI) for manufacturing. Our award winning technology embeds unique adaptations and advancements of deep learning, enabling AI to have a significant, practical, impact on the factory floor. DataProphet’s solutions are built to be adapted and integrated into existing environments, making it possible for our digital transformation team to take your operations from zero to AI. We understand manufacturing and that real impact is achieved with pre-emptive actions because real-time is often too late. For more information, visit www.dataprophet.com Why join DataProphet? You'll work on technically challenging problems where AI moves beyond experimentation and creates measurable real-world impact. You'll have meaningful ownership, work alongside highly capable colleagues across Data Science and Engineering, and have the opportunity to apply your skills across new problems, use cases and domains. Curiosity, continuous learning and collaboration are central to how we work. Our team works together from our DeWaterkant, Cape Town office in a professional, supportive environment designed to help people do their best work. Role Overview: We are looking for a Data Scientist to solve complex, real-world problems using advanced analytics, statistics and machine learning. You will work across the data science lifecycle — from understanding and framing the problem and exploring data through to developing, validating and helping deploy models that create measurable operational and business impact. Roles and responsibilities will include, but are not limited to: Use data science, statistics and machine learning to solve complex real-world and business problems. Translate business questions into structured analytical and modelling problems. Explore and assess data sources for quality, relevance and predictive value. Develop statistical, forecasting, predictive modelling, simulation, machine learning and optimisation solutions. Design and engineer features that improve model performance and robustness. Design experiments and apply appropriate statistical methods to validate hypotheses and model performance. Build, evaluate and validate models intended for real operational use — not simply experimentation. Work with Data Engineers and Software Engineers to move models and solutions towards production. Monitor and evaluate model performance and contribute to their ongoing improvement. Communicates findings to business users, using data visualisation techniques to share solutions. Communicate findings, recommendations, assumptions and limitations clearly to technical and non-technical stakeholders. Designs quantitative advanced analytics models that answer business questions and/or discover opportunities for improvement, increased revenue or reduced costs. Qualifications & Experience: Bachelor's / Honours /Masters /PhD degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, or a related quantitative field Relevant certifications (e.g., in Python, SQL, ML fundamentals) 2–5 years of hands-on data science experience Track record of owning problems end-to-end: framing the business question, building/validating models, and communicating results to non-technical stakeholders Some exposure to production or semi-production environments Experience developing and evaluating machine learning or statistical models using real-world data. Exposure to models that have been deployed or used operationally, rather than exclusively notebook-based analysis, is advantageous. Relevant technical or cloud certifications are advantageous. Core Skills: Strong Python skills, including experience with common data science and machine learning libraries such as pandas, polars, NumPy and scikit-learn. Strong SQL skills and the ability to work confidently with complex datasets. Strong grounding in statistics, probability and applied machine learning. Experience with supervised and unsupervised learning, model selection, feature engineering, model evaluation and validation. Understanding of experiment design, hypothesis testing and statistical rigour. Ability to select appropriate analytical or modelling approaches and explain the trade-offs involved. Ability to structure and solve loosely defined problems independently. Strong data visualisation, communication and storytelling capability. Ability to translate technical findings into clear business insights and recommendations. Experience with version control and collaborative development practices such as Git and code review. Exposure to cloud platforms such as AWS, Azure or GCP, distributed processing tools such as Spark, and model deployment or MLOps practices is advantageous. Experience with version control and collaborative development practices such as Git and code review. Familiarity with Linux & shell scripting a strong plus