Technical Lead - Data & AI Products

• 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.

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• 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.

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.

• Deep Learning
• Generative AI
• Statistical Modeling
• Predictive Analytics
• MLOps

• 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

• Preferred Automotive Experience Experience in one or more of the following domains: Automated Driving
• ADAS
• Fleet Learning
• Vehicle Telemetry

• Data Mining
• Data Curation
• Scenario Discovery
• Vehicle Validation

• Customer Obsession
• Executive Communication

• 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