The ideal candidate should possess strong expertise in statistical modeling, machine learning, AI, and emerging Agentic AI frameworks, along with experience in solving real-world automotive or manufacturing problems such as predictive maintenance, quality analytics, supply chain optimization, or connected vehicle use cases.
- Develop, validate, and deploy machine learning and AI models to solve business challenges.
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Apply statistical techniques (hypothesis testing, regression, Bayesian methods) to derive insights from complex datasets.
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Design and implement end-to-end data science pipelines, including data ingestion, feature engineering, model training, evaluation, and deployment.
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Build and operationalize Agentic AI systems (autonomous agents, multi-agent workflows, LLM-based reasoning systems).
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Work on time-series forecasting, anomaly detection, and predictive analytics for manufacturing/automotive use cases.
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Collaborate with cross-functional teams including data engineering, product, domain experts, and business stakeholders.
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Interface with IoT, telematics, MES, ERP, and connected vehicle platforms for data-driven insights.
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Ensure scalability and performance by deploying models using cloud-based solutions (Azure/AWS/GCP).
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Communicate findings effectively through visualizations, dashboards, and presentations.
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Stay current with advancements in AI/ML, including GenAI and Agentic AI ecosystems.
