AI Lead
About the Role
Location Romania Bucuresti Bucharest Remote vs. Office Hybrid (Remote/Office) Company Siemens Energy Organization Transformation of Industry Business Unit Electrification - Automation - Digitalization Full / Part time Full-time Experience Level Experienced Professional A Snapshot of Your Day
As our Advanced Analytics & AI specialist, you will be both a hands-on specialist and the technical leader of the team. You will define the vision, architecture, and roadmap for advanced analytics and AI solutions, while guiding the team in designing, building, and operating scalable, production-grade data and AI products.
How You’ll Make an Impact
Lead the technical direction for advanced analytics and AI initiatives, from problem framing to solution design, implementation, and industrialization.Architect and oversee robust data and ML pipelines, ensuring reliability, scalability, and performance across cloud environments (, Azure, GCP) and modern data platforms.Provide technical leadership to the analytics and AI team, mentoring data scientists, ML engineers, and analysts, and promoting engineering excellence and best practices.Partner with business stakeholders to identify high-impact AI and analytics opportunities, translating business needs into measurable, data-driven solutions.Define and enforce standards for model governance, MLOps, data quality, security, and compliance, ensuring responsible and explainable AI.Promote a data- and AI-driven culture across the organization through communication, enablement, and internal consultancy.What You Bring
Strong experience in advanced analytics and applied AI, with a track record of delivering end-to-end projects from discovery to production.Proven hands-on expertise with modern data and AI stacks (, cloud data platforms, Spark, Python, SQL, ML frameworks, and BI tools such as Power BI or similar).Demonstrated experience leading or technically guiding cross-functional data teams, including mentoring, coaching, and elevating technical standards.Solid understanding of data architecture, data engineering, and MLOps concepts, including CI/CD, observability, and lifecycle management for models.Excellent stakeholder management and communication skills, with the ability to influence and align both technical and business teams at different levels of the organization.
