• Apply machine learning and statistics expertise to solve analytical problems mainly in finance and location analytics.
• Work closely with a software development team to convert analytic results into software products.
• Use machine learning expertise to conceptualize new solutions for financial or other problems.
• Communicate and document progress and results to the customers and produce analytical reports.
• BS or preferable MS degree in Computer Science, Computational Science, Data Science or general Engineering or related disciplines from a reputable university.
• Solid theoretical understanding of machine learning concepts and algorithms.
• Practical experience with supervised and unsupervised machine learning algorithms (e.g. random forest, multilayer perceptron, SVM, k-means clustering, PCA).
• Experience with Python 3 and its scientific computation and visualisation packages (e.g. numpy, pandas, scikit-learn, matplotlib).
• Skills in data preprocessing, cleaning and data visualisation.
• Proficiency (theoretical know-how) in probability and statistics.
• Good working experience with SQL is necessary to extract and work with data residing in RDMSes.
• Fluency in written/spoken presentation of analytical results in English.
Nice to Haves
• Experience with deep learning libraries is a plus (e.g. TensorFlow, PyTorch).
• Experience with an additional, OOP language is a plus (e.g. C#, C++).
• Experience with distributed big-data computing frameworks is a plus (e.g. Hadoop, Spark, Kafka).
• Experience with financial data and solutions experience is a plus.
• Experience with Time-Series Forecasting is a big plus.