Senior AI/Data Science Engineer

Job description

Careers that change lives start here. Medtronic is a global leader in healthcare technology with a Mission to alleviate pain, restore health, and extend life. Our 95,000 employees work across more than 150 countries to put patients first — developing innovative medical technologies that improve the lives of 72+ million patients each year. Your unique talents will help shape the future of healthcare while building a career grounded in purpose, growth, and impact. A Day in the Life As a Senior AI/Data Science Engineer, you will play a critical role in developing data, analytics, and AI solutions that help solve complex business challenges and support Medtronic's Mission to alleviate pain, restore health, and extend life. You will work at the intersection of business, data, and technology, partnering with global stakeholders to apply advanced analytics, machine learning, and generative AI where they create meaningful impact. The Senior AI/Data Science Engineer designs, develops, deploys, and improves advanced analytics, machine learning, and AI solutions for the Commercial domain. Working with product owners, business analysts, data engineers, architects, and business stakeholders, the role translates business needs into clear analytical approaches and reliable AI-enabled products. Responsibilities may include the following and other duties may be assigned. Business Partnership & Value Realization • Translate business objectives into practical analytics and AI solutions. • Communicate insights and recommendations to technical and non-technical audiences. • Embed deployed solutions into business processes to drive measurable impact. Data Science & AI Solution Development • Lead end-to-end delivery of advanced analytics, machine learning, and AI solutions. • Develop descriptive, diagnostic, predictive, and prescriptive models using statistical and machine learning techniques. • Perform exploratory analysis, experimentation, feature engineering, optimization, and A/B testing. • Build generative AI solutions, including LLM-based applications, retrieval-augmented generation, and agentic workflows. • Define evaluation, performance, bias, and monitoring frameworks. AI Engineering & Product Delivery • Lead AI product delivery from ideation to production and lifecycle management. • Turn models and requirements into production-ready code and enterprise AI applications. • Develop and maintain machine learning pipelines, APIs, reusable libraries, and enterprise AI services. • Collaborate with architects, data engineers, and product teams to deliver secure, reliable, and maintainable AI solutions. • Apply software engineering and MLOps practices, including testing, version control, CI/CD, and deployment automation. Scalable Data & AI Platforms • Use cloud platforms, distributed computing, and modern data and AI ecosystems to build robust enterprise solutions. • Optimize AI products for performance, reliability, security, maintainability, and operational excellence. • Create reusable frameworks, accelerators, standards, and best practices to improve delivery speed and quality. Leadership & Technical Excellence • Mentor and coach data scientists and analytics professionals. • Guide solution quality through design reviews, best practices, and architectural input. • Foster a culture of innovation, experimentation, continuous learning, and responsible AI. • Advance data science and AI practices across the organization. Required Knowledge and Experience: • 7+ years of IT experience with a Bachelor's Degree in Engineering, MCA, MSc or MBA • Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Analytics, or another quantitative field is preferred. • 7+ years of experience in data science, advanced analytics, AI, or machine learning. • Strong proficiency in Python, SQL, statistical modeling, machine learning, and AI methodologies. • Experience developing and deploying production-grade machine learning and AI solutions and translating business needs into technical solutions. • Strong communication, stakeholder management, and collaboration skills in global, cross-functional environments. • Experience with generative AI, LLM-based applications, retrieval-augmented generation, or agentic AI concepts. • Experience with cloud and analytics platforms such as Snowflake. • Experience with machine learning frameworks (e.g., Scikit-Learn, TensorFlow, PyTorch, XGBoost) and MLOps practices. • Experience developing production-grade AI applications, APIs, and software services. • Knowledge of responsible AI, AI governance, and healthcare, MedTech, commercial, customer, or operational analytics domains. • Strategic thinking and business acumen. • AI, machine learning, and data science expertise. • Technical guidance and software engineering excellence. • Stakeholder management, influence, and communication. • Innovation, continuous learning, and problem solving.   Physical Job RequirementsThe above statements are intended to describe the general nature and level of work being performed by employees assigned to this