Data Scientist AI - 36 Months

Synopsis Join us in shaping the future of AI and data-driven innovation. As a Data Scientist AI, you will develop machine learning and generative AI solutions that solve complex business challenges, automate processes, and unlock valuable insights from data. Working closely with business and technology teams, you will take ideas from concept to production, delivering scalable, secure, and impactful AI products. Accountabilities Design, develop, deploy, and monitor machine learning and AI solutions addressing business-critical challenges. Translate business requirements into data science and AI use cases, defining success measures and evaluation criteria. Analyze large and complex datasets to generate actionable insights, predictive models, and decision-support solutions. Develop and optimize machine learning models through feature engineering, validation, testing, and performance tuning. Build and deploy LLM-powered, retrieval-augmented, and agentic AI applications for automation, knowledge management, and business process optimization. Design document intelligence, search, and information retrieval solutions using structured and unstructured enterprise data. Establish evaluation frameworks, monitoring, observability, and governance controls to ensure solution quality, reliability, and cost efficiency. Collaborate with engineering teams to deliver scalable, secure, and production-ready AI applications integrated with enterprise platforms and systems. Lead discovery workshops, process assessments, and stakeholder engagements to identify and prioritize AI opportunities. Communicate findings, recommendations, and model outcomes through effective storytelling, visualization, and executive-level presentations. Promote best practices in machine learning, AI engineering, responsible AI, and data governance. Stay current with emerging trends and technologies across artificial intelligence, machine learning, generative AI, and advanced analytics.

Education & Experience Bachelor's degree in Computer Science, Data Science, AI, Mathematics, Statistics, or a related discipline. 5+ years of experience in Data Science, Machine Learning, or AI, including 2+ years delivering production-grade Generative AI solutions. Strong Python skills with hands-on experience in machine learning frameworks such as Scikit-learn, TensorFlow, or PyTorch. Experience building and deploying LLM, RAG, and agentic AI solutions using platforms such as Azure OpenAI, OpenAI, LangChain, or Semantic Kernel. Solid understanding of machine learning, statistical modelling, predictive analytics, and AI evaluation practices. Experience deploying AI solutions in cloud environments (Azure, AWS, or GCP) with knowledge of APIs, CI/CD, and containerization. Experience integrating AI solutions with enterprise systems and business processes. Strong communication, stakeholder management, and problem-solving skills, with the ability to work independently in a fast-paced environment.

Design, develop, deploy, and monitor machine learning and AI solutions addressing business-critical challenges. Translate business requirements into data science and AI use cases, defining success measures and evaluation criteria. Analyze large and complex datasets to generate actionable insights, predictive models, and decision-support solutions. Develop and optimize machine learning models through feature engineering, validation, testing, and performance tuning. Build and deploy LLM-powered, retrieval-augmented, and agentic AI applications for automation, knowledge management, and business process optimization. Design document intelligence, search, and information retrieval solutions using structured and unstructured enterprise data. Establish evaluation frameworks, monitoring, observability, and governance controls to ensure solution quality, reliability, and cost efficiency. Collaborate with engineering teams to deliver scalable, secure, and production-ready AI applications integrated with enterprise platforms and systems. Lead discovery workshops, process assessments, and stakeholder engagements to identify and prioritize AI opportunities. Communicate findings, recommendations, and model outcomes through effective storytelling, visualization, and executive-level presentations. Promote best practices in machine learning, AI engineering, responsible AI, and data governance. Stay current with emerging trends and technologies across artificial intelligence, machine learning, generative AI, and advanced analytics.