– Bachelor’s degree in Statistics, Computer Science, Mathematics, Engineering or equivalent.
– Knowledge of the latest technologies and trends on Data Science, including artificial intelligence, machine learning and data fabrics
– Knowledge and experience in applied statistics skills, such as distributions, statistical testing, regression
– Good understanding of machine learning techniques and algorithms and their real-world advantages/drawbacks, such as RNNs, LSTMs, Random Forest, k-NN, SVM, Decision tree, etc.
– Experience with Time Series forecasting.
– Good data mapping, data transfer and data migration skills. Basic understanding of analytics software (e.g., Python).
– Experience with common data science toolkits, such as Pandas, NumPy, Scikit-learn, etc.
– Good knowledge of data visualization techniques and data visualization tools such as Matplotlib, Plotly, GGplot, Seaborn, etc.
– Knowledge of SQL/NoSQL Databases extracting, cleaning, preparing, and modelling data.
– Exposure with scalable big data, manipulate them and extract insights from them will be considered.
– Basic understanding of business requirements and data science objectives.
– Good communication and presentation skills.
– Strong problem-solving skills and critical thinking.