CAIA's Career Center is an easy-to-use, comprehensive resource connecting job seekers with employers in the growing AI field. Use your knowledge and credibility to advance your career or build a talented team for your organization. Opportunities targeted to CAIA Charterholders are prioritized.
In order to search for jobs specifically for CAIA Charterholders or those pursuing the CAIA Charter please enter “CAIA” in the search panel.
This will enable you to search for CAIA specific roles globally.
Design, implement and evaluate advanced statistical and machine learning models and approaches for application to various business problem statements
Communicate findings from analytical modelling results to relevant stakeholders that will give insights to improve decision making and drive business performance
Implement analytical models into production by collaborating with relevant stakeholders
Develop processes and tools to monitor and analyse model performance, as well as implement improvements as needed.
Provide support in building the foundation of technical analytics capabilities within Enterprise Data Analytics department.
Drive analytics innovation by keeping abreast of industry's trends, evaluating and adapting new and improved data science approaches for the business.
Develop in depth understanding of the business and be able to advise the business on the right analytics approach by participating in business discussions and presentations as applicable.
Bachelor's degree holder, ideally in STEM disciplines (Science, Technology, Engineering, Math); graduate degree in Statistics, Applied Math, Data Science or other related quantitative field is preferred.
Approximately 3-5 years' of working experience in data science.
Experience using programming languages (e.g. Python, R, SQL) to manipulate data and draw insights from large data sets.
Experience with business intelligence tools (e.g. Tableau), Excel, PowerPoint.
Knowledge of a variety of machine learning techniques (clustering, decision tree, random forest, artificial neural networks, etc.) and their real-world advantages/drawbacks.
Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
Strong analytical skills, deductive reasoning, problem solving and critical thinking skills.
Excellent relationship management, strong team building, and the ability to work across business units and functions to drive positive business outcomes.
Project management skills and ability to manage multiple priorities.
Strong sense of urgency and accountability to drive business outcomes.
Passionate about working with numbers.
A drive to learn and master new technologies and techniques