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Technical Lead - Data & AI Products

Mercedes-Benz Research and Development India Private Limited
Bengaluru, IND
Full-time
Senior · 12+ years experience
Onsite
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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.

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

• 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

• 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

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