Currently pursuing a Bachelor's Degree in Data Science, Computer Science, Statistics, Mathematics, Artificial Intelligence, Information Technology, or a related discipline.
Basic understanding of statistics, data analysis, and machine learning concepts.
Familiarity with Python or R for data analysis.
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A Bachelor’s degree in Finance, Accounting, Business, Economics, Data Analytics, Information Systems, or a related discipline. Candidates with data analytics, Power BI, and automation experience alongside compliance or risk knowledge will have an advantage.
At least 2 years’ relevant experience in risk management and/or compliance within fund management, banking, or insurance.
Strong knowledge of applicable policies, laws, regulations, and professional standards, preferably within the capital markets environment.
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Bachelor’s degree in Business Analytics, Data Analytics, Finance, Banking, or a related discipline
Mandatory minimum 3 to 5 years of experience within debt collection operations, collections strategy, banking recovery, or as a functional analyst/consultant for accounts receivable software
Proven track record of learning complex enterprise software platforms quickly and understanding how technical configurations translate into business results
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2–5 years of professional experience in project management, product management, business consulting, or technology delivery. Prior hands-on experience in engineering, software development, or technical operations is preferred.
Experience working on AI, data platforms, analytics, or digital transformation projects is strongly preferred.
Prior experience in startups, technology companies, consulting firms, or enterprise environments is an advantage.
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Lead client-facing data science, analytics, and data engineering projects from scoping through deployment and handover.
Design solution architectures using our low-code data science studio, visual ETL, and lakehouse with CDC, balancing maintainability and scale.
Write maintainable Python to extend and integrate platforms, build APIs, custom transformations, model logic, and automation where visual tools are insufficient.
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