Data and AI Lead

Responsibilities:

Diagnostics Data Engineering & Architecture

Diagnostics Strategy & Transformation

• Define and execute the Data science, machine Learning and AI-powered diagnostics roadmap for DTC/DID evolution Transition from rule-based diagnostics* predictive and prescriptive diagnostics
• Drive data-centric architecture for vehicle health monitoring and diagnostics intelligence
• Collaborate with OEM stakeholders to embed AI into vehicle lifecycle (in-vehicle + cloud)

Data Science & AI Leadership

• Lead development of ML/AI models for:
• • Fault detection and classification
  • Root cause analysis
  • Remaining Useful Life (RUL) prediction
  • Anomaly detection in telemetry and ECU signals
• Apply advanced analytics on:
• • Time-series vehicle data
  • CAN/LIN/Ethernet communication data
  • Sensor fusion data streams
• Establish model lifecycle management (MLOps) for continuous learning and deployment

Diagnostics Strategy & Engineering Enablement

• Support definition and continuous improvement of vehicle diagnostics strategy for ICE, Hybrid, and EV platforms.
• Collaborate with system and software teams to:
• • Improve onboard diagnostics (OBD) logic
  • Enhance remote and OTA diagnostics capabilities
  • Enable predictive diagnostics using advanced analytics/ML
• Translate product and engineering requirements into data-driven diagnostics solutions.

Platform & Architecture

• Define scalable data architectures for diagnostics:
• • Edge (in-vehicle) + Cloud (off-board analytics)
• Work with engineering teams to build:
• • Diagnostic data lakes
  • Real-time streaming pipelines
• Ensure integration with:
• • Vehicle telemetry platforms
  • Digital twin ecosystems

Tooling, Automation & Advanced Analytics

• Develop and deploy tools for:
• • Diagnostics data mining and automation
  • Fault pattern recognition
  • Data-driven decision-making
• Leverage AI/ML techniques for predictive failure detection.
• Enable self-service data analytics platforms for engineering users.

Requirements:

Required Skills:

• Bachelor’s or master’s degree in Computer Science / Data Engineering
• 5–10 years of experience in automotive, data engineering, or diagnostics domain.
• Strong technical expertise in:
• • Python, SQL
  • Data platforms (Spark, Databricks, or similar)
  • Cloud ecosystems (Azure preferred )
• Experience in handling large-scale automotive datasets.
• Strong analytical and problem-solving skills
• Ability to convert complex data into engineering insights
• Excellent stakeholder communication and collaboration
• Understanding of automotive system architecture
• Agile mindset with a focus on innovation and continuous improvement

Preferred Skills:

• Hands-on experience with:
• • Data Science and AI and ML
• Masters in data science, AI and ML

Compensation and Benefits:

Along with competitive pay, as a full-time KPIT employee, you are eligible for the following benefits:

• Geo Blue PPO and HSA plan.
• MetLife – Dental and Vision plan.
• Healthcare and Dependent care flexible spending account(FSA).
• 401k with employer match.
• Company-paid Basic Life and Long-term disability insurance.
• Voluntary benefits include Critical Illness, Hospital indemnity, accident insurance, theft, and legal service.
• Employee Assistance Program.
• Paid Holidays.
• Employee discounts and perks.
• Gym benefit.

AUTOMOTIVE,PYTHON,SQL,SPARK,DATABRICKS,AZURE

DATA SCIENCE,AI,ML