Data Scientist (AI Engineer)

• Support workshops to understand business goals, constraints, and data landscapes; translate them into prioritized AI use cases and a phased delivery plan (PoC → MVP → Production).
• Support the design of solution architecture, effort estimation, and clearly articulate technical and business value during proposal development.
• Collaborate with cross-functional teams to understand business challenges and translate them into actionable analytical solutions.
• Implement, and deploy machine learning models across various domains, covering both Traditional and Generative AI use cases.
• Rapidly prototype and validate ML/GenAI solutions (e.g., classification, forecasting, NLP, RAG, summarization, document intelligence, agentic workflow).
• Develop robust, maintainable, and production-ready machine learning workflows, with a focus on MLOps practices.

• Bachelor's degree in STEM fields (e.g., Engineering, Computer Science, Statistics, Mathematics, Economics, or related disciplines).
• Hands-on experience delivering ML/AI solutions; consulting/client-facing experience preferred.
• Proven hands-on experience with Python and SQL for data analysis and model development; familiarity with cloud platforms for scalable analytics solutions.
• Practical experience across the AI/ML lifecycle: data preparation, modeling, evaluation, deployment, and monitoring.
• Ability to translate complex analytical insights into clear, actionable business recommendations for diverse stakeholders.
• Good to have certifications

• AI / Data Science Specific e.g., Google Professional Machine Learning Engineer, Microsoft Certified: Azure AI Engineer Associate
• Cloud & Data Platforms e.g., Google Professional Data Engineer, Microsoft Certified: Azure Data Scientist Associate

KPMG