We are seeking a highly motivated and academically strong Data Scientist – Level 1 to join a team of talented data professionals solving complex business challenges through data, analytics, and machine learning. This opportunity is ideal for recent graduates or early-career professionals who have a strong foundation in mathematics, statistics, computer science, engineering, or a related STEM field and are eager to apply their knowledge to real-world problems. We are specifically looking for high-performing graduates who have achieved excellent academic results from reputable universities and institutions. ​ What You'll Do As a Data Scientist – Level 1, you will work alongside experienced data scientists and engineers while developing your technical and problem-solving capabilities. Your responsibilities will include: - Performing exploratory data analysis (EDA) to identify patterns, trends, and insights - Assisting in the design, development, and evaluation of machine learning models - Cleaning, preparing, and transforming data for analysis and modelling - Developing analytical solutions to solve business problems - Writing clean, maintainable, and well-documented Python code - Conducting statistical analysis and interpreting results - Presenting findings and recommendations clearly to technical and non-technical stakeholders - Collaborating with cross-functional teams on data-driven initiatives - Continuously learning and applying new techniques, tools, and methodologies ### Requirements - Bachelor's Degree (BSc, BEng, BCom Honours, BSc Honours, MSc or equivalent) in: Data Science, Computer Science, Statistics, Mathematics, Engineering, Actuarial Science, Physics Or another quantitative STEM discipline - Excellent academic record throughout university studies - Degree obtained from a recognised and reputable university or higher education institution - Strong results in mathematics, statistics, machine learning, programming, or related quantitative subjects - Solid understanding of data science and machine learning fundamentals - Proficiency in Python - Experience with libraries such as: Pandas, NumPy, Scikit-learn Advantageous Skills - Exposure to machine learning projects through academic work, internships, personal projects, or competitions - Experience with SQL and relational databases - Familiarity with cloud platforms such as AWS, Azure, or GCP - Exposure to TensorFlow, PyTorch, or other machine learning frameworks - Knowledge of data visualisation tools such as Power BI, Tableau, or similar - Participation in Kaggle competitions, research projects, or published academic work