A data science professional with a strong problem-solving mindset and the ability to translate complex business questions into analytical solutions.
Experienced in working collaboratively within cross-functional teams and comfortable sharing knowledge and feedback.
Brings at least six years of hands-on experience developing analytical models using large-scale datasets in a commercial, consumer, or digital environment.
Proficient in Python and PySpark, with end-to-end experience from problem definition through to production deployment.
Strong grounding in statistical learning and machine learning techniques, including regression, tree-based models, clustering, NLP, deep learning, and reinforcement learning.
Experienced with modern machine learning libraries such as scikit-learn, MLlib, TensorFlow, PyTorch, H2O, and R.
Knowledgeable in data warehousing and big data technologies, including SQL, NoSQL, Spark, and related ecosystems.
Familiar with experimentation, A/B testing, and developing analytical reports and insights.
Exposure to data visualisation tools such as Qlik, Shiny, Dash, D3, Tableau, or Power BI is valued
Experience with cloud platforms (ideally Google Cloud) and containerisation technologies is desirable.
