Job/Position SummaryResponsibilities: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 1 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.
ESSENTIAL SKILLS /COMPETENCIES0AUTOMOTIVE
0PYTHON
0SQL
0SPARK
0DATABRICKS
0AZURE
PREFFERED SKILLS /COMPETENCIES0DATA SCIENCE
0AI
0ML
