Data Scientist - AI & Innovation Engineer

The DBVA platform is central to the Business Value Assurance function within IFS and the partner community, used daily by 100+ employees. This role will be critical in the replatforming and enablement of a solution at the forefront of AI by embedding AI/ML capabilities and AI agents into the tool. Long-term you, as part of a small, fast paced and high-impact team, will be responsible for ensuring secure, responsible and innovative use of AI to deliver real business value. In this role, you are going to drive applied AI innovation and be responsible for embedding AI and advanced data capabilities into the platform. You will drive use cases around automation, copilots, predictive insights, natural language interaction and AI agents. You will work closely with business stakeholders to translate requirements into working AI-enabled solutions. A strong emphasis is placed on Azure based deployment, security and usability. Key Responsibilities: Prototype and deliver AI features (copilots, NLP, AI agents) with Azure AI/OpenAI Deploy and maintain ML/AI components in Azure (App services, Azure ML, SQL integration) Prepare and manage data for secure, multi-tenant use cases (RLS, B2B segregation) Collaborate with System Engineer to integrate AI into React/Node.js solution Translate business needs into prototypes and iterate quickly with users Communicate outcomes, prepare demos and support training materials creation Ensures ethical, secure and responsible AI usage aligned with company standards Overall Competencies: Strong Python (or equivalent) skills with applied ML/AI frameworks (such as PyTorch, TensorFlow, Scikit-learn) Practical experience deploying ML/AI in Azure Familiarity with Azure OpenAI services, LLM APIs and API-based integrations Strong SQL data handling skills in enterprise settings Ability to prototype rapidly and iterate with business stakeholders Understanding security, compliance and responsible AI practices Communication skills to explain AI outcomes to business stakeholders Qualifications Essential: Master’s degree in data science, Computer Science, Applied Mathematics or equivalent experience 3-5+ years hands-on experience in data science/AI engineering Proven experience of deploying AI/ML into production, preferably in Azure Proven record of delivering business facing AI features (predictive models, copilots, agents or automation) AI Integrations: Azure OpenAI, LLM APIs or NLP Experience working with multi-tenant or enterprise data solutions Strong English Desirable: Node.js/JS for API integrations Advanced AI/ML libraries: OpenMMLab, DeepStream Prototyping with AI agents and copilots Delivered AI features tied to business use cases