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Tier-1 brand, generalist Data Scientist title, and Bangalore metro increase candidate density.
Strong automotive/manufacturing and IoT domain requirements reduce cross-industry transferability.
Mandates Python, ML/AI expertise, model deployment, and automotive domain experience, enforcing strict technical filters.
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Develop, validate, and deploy machine learning and AI models specifically for automotive or manufacturing business challenges such as predictive maintenance and supply chain optimization.
Design and implement end-to-end data science pipelines, including data ingestion, feature engineering, model training, evaluation, and deployment on cloud platforms (Azure preferred, AWS, GCP).
Build and operationalize Agentic AI systems (autonomous agents, multi-agent workflows, LLM-based reasoning) and integrate with IoT, telematics, MES, ERP, and connected vehicle platforms.
Mandatory programming skill: Python with libraries Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch.
Experience applying statistical techniques including hypothesis testing, regression, Bayesian methods.
Domain experience required: automotive or manufacturing, especially in predictive maintenance, quality analytics, supply chain optimization, or connected vehicle analytics.
Work Experience Required: Not explicitly mentioned in the JD.
Demonstrates strong expertise in applied machine learning, AI, and emerging Agentic AI frameworks relevant to real-world industrial use cases in automotive or manufacturing.
Experienced in cloud-based model deployment and building production-grade AI/ML systems, indicating operational focus and ability to scale solutions.
Skilled at communicating complex model insights effectively to non-technical stakeholders and managing cross-functional teams including data engineering and business units.