Senior Machine Learning Scientist
QlooFull Time
Senior (5 to 8 years), Expert & Leadership (9+ years)
Candidates must possess a good degree in a scientific or numerate discipline such as Computer Science, Physics, Mathematics, or Engineering, or have equivalent work experience. Experience in applying practical machine learning algorithms to real-world data, implementing statistical models and analytical algorithms in software, and using Python, Java, R, or another major programming language for data analysis, machine learning, or algorithm development is essential. Strong technical and analytical skills with the ability to quickly learn new technologies and concepts, excellent problem-solving abilities, and clear, concise written and verbal communication skills are required. The ability to manage and prioritize personal workload, practical experience handling and mining large, diverse datasets, and constructive participation in system architecture/design discussions from an analytical perspective are also necessary. A Ph.D. or postgraduate qualification and experience in a Linux command line environment or using SQL are advantageous.
The Data Scientist will build advanced statistical models and algorithms to infer and predict individual customer behaviors in real-time using transaction data. This customer-facing role involves close collaboration with clients to understand their data and challenges. Responsibilities include applying analytical theory to diverse real-world problems on large datasets, generating integral work products from project kickoff to live deployment, and developing statistical models and algorithms for integration within Featurespace products. Key tasks involve end-to-end processing and modeling of large customer datasets, producing reports, presentations, and visualizations to feedback analytic output to customers, and evaluating and improving analytical results on live systems. The role also requires developing an understanding of industry data structures and processes, working with engineering teams to support and enhance analytical infrastructure, testing analytical models and their integration within the ARIC platform, and providing input into future data science strategy and product development.
Global digital payment network provider
Visa operates a global digital payment network that facilitates electronic payments for millions of people daily. The company connects consumers, businesses, financial institutions, and governments, allowing them to make transactions using Visa cards. Each time a card is used, Visa earns money through transaction, service, and data processing fees. Unlike many competitors, Visa focuses on expanding access to financial services for underserved communities and supporting local economies. The company's goal is to promote financial inclusivity and drive sustainable commerce, ensuring that more people can participate in the global economy.