Technical Lead – Data & AI Products (Automated Driving, Data Mining & Customer Platforms)

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.

• 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
• 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 Roadmap execution; KPI achievement; User satisfaction; Technical Excellence; Platform scalability and reliability; Data quality improvements; AI solution effectiveness.