Technical Lead - Data %26 AI Products at Mercedes-Benz… · Bengaluru

Technical Lead – Data & AI Products (Automated Driving, Data Mining & Customer Platforms) Location India / Global Capability Center (GCC) Function Data, AI & Digital Engineering Reports To Senior Manager of Data & AI Platforms Role Overview We are seeking a highly motivated and experienced Technical Lead to drive the intersection of Data Science, Data Engineering, & Product Management, within our automotive software and data ecosystem. This role requires a unique blend of technical excellence, product thinking, customer focus, and stakeholder management. The successful candidate will lead the development of next-generation data products and platforms that enable Automated Driving, Fleet Learning, Vehicle Intelligence, Data Mining, and AI-driven decision-making. The ideal candidate combines deep technical expertise with strong business acumen and can effectively influence stakeholders across engineering, product, operations, and executive leadership teams. Key Responsibilities Product Ownership & Business Alignment • Own the vision, roadmap, and lifecycle of enterprise-scale data products. • Translate business requirements into scalable technical solutions. • Drive adoption and value realization across global stakeholders. Data Science & AI Leadership • Lead development of AI and Machine Learning solutions for large-scale automotive datasets. • Enable advanced analytics, predictive modeling, scenario mining, and intelligent data selection. • • Establish best practices for model development, deployment, monitoring, and governance. Drive adoption of MLOps and GenAI capabilities to improve engineering efficiency and product outcomes. Data Engineering & Platform Leadership • Lead design and implementation of cloud-native data platforms handling petabyte-scale sensor and vehicle data. Job Description Page 1 and vehicle data. Drive architecture decisions for batch and streaming data pipelines. • Ensure platform scalability, reliability, observability, and security. • Champion data quality, metadata management, governance, and compliance. • Customer & Stakeholder Management Act as the primary interface between business stakeholders, engineering teams, and executive leadership. • Facilitate alignment across multiple organizations and geographical locations. • Build trusted partnerships with internal and external stakeholders. • Team Leadership & Organizational Development Lead multidisciplinary teams across Data Science, Data Engineering & Product Management, and Program Management. • Required Qualifications Education Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or related field. • MBA or Product Management certification is advantageous. • Experience 12–18 years of experience in Data Science, Data Engineering, Software Engineering, or AI-related domains. • 5 years of experience leading cross-functional teams. • Proven experience managing large-scale technology products • Experience working in global organizations and matrix structures. • Technical Skills Data Science Machine Learning • Deep Learning • Generative AI • Statistical Modeling • Predictive Analytics • MLOps • Data Engineering Apache Spark • Kafka • Databricks • Delta Lake • Airflow • Snowflake • Lakehouse Architectures • Cloud & Platform Engineering AWS / Azure / GCP • Kubernetes • Docker • Infrastructure as Code • Job Description Page 2 Infrastructure as Code • CI/CD • Product Management Agile Product Development • Customer Journey Mapping • Business Case Development • Preferred Automotive Experience Experience in one or more of the following domains: Automated Driving • ADAS • Fleet Learning • Vehicle Telemetry • Sensor Data Processing • Data Mining • Data Curation • Scenario Discovery • Vehicle Validation • Software Defined Vehicle (SDV) • Automotive AI Platforms • Leadership Competencies Systems Thinking • Customer Obsession • Executive Communication • Influencing Without Authority • Stakeholder Management • Success Metrics The successful candidate will be measured on: Business Impact Product adoption and customer value realization • Operational efficiency improvements • Strategic initiative delivery • Product Outcomes Roadmap execution • KPI achievement • User satisfaction • Technical Excellence Platform scalability and reliability • Data quality improvements • AI solution effectiveness