Applied Data Scientist leading AI-based solutions for operational efficiency at Control Risks. Collaborating with Data Engineering on advanced machine learning techniques for scalable enterprise-wide AI solutions.
Responsibilities
To lead the development and implementation of AI-based solutions aimed at improving operational efficiency within Control Risks.
The Applied AI Scientist will work within the broader Data Engineering team and focus on the use of advanced AI and machine learning techniques to build, analyze, and deploy scalable, enterprise-wide AI solutions
Requirements
Preferably 2+ years of experience working with different AI capabilities and showcasing your passion both at work and outside work in the development of highly complex AI models (NLP, LLMs, Deep learning etc).
2+ years of experience working within a Data Engineering function, having proficiency in analytical and pipelining tools within platforms such as Microsoft Azure / AWS.
Technical proficiency in programming languages and frameworks commonly used in NLP and AI (e.g., Python, TensorFlow, PyTorch).
Familiarity with Microsoft Fabric, Azure ML Studio, and MLOps principles to build scalable workflows for effective ML monitoring systems.
Excellent communication, problem-solving, and analytical skills with good fluency in English.
Ability to work collaboratively in a global team environment.
Good interpersonal skills, possessing the confidence to build relationships with all levels of stakeholders.
Understanding of Git Version control, CI/CD, Agile development, data security, and governance.
Bachelor’s degree in AI, Computer Science, Data Science, Statistics, Engineering or a related field.
Certifications in AI, data analysis, or related areas are a plus.
Knowledge of low-code development and business automation is advantageous.
Experience with cloud platforms such as Azure is preferred.
Experience in development on both Dataverse and other sources like SharePoint.
Any Power Platform certification will be an advantage.
Experience in applying RAG solutions for commercial data.
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