Description
The ideal candidate should possess solid 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.

Responsibilities
Develop, validate, and deploy machine learning and AI models to solve business challenges.
Apply statistical techniques (hypothesis testing, regression, Bayesian methods) to derive insights from complex datasets.
Design and implement end-to-end data science pipelines, including data ingestion, feature engineering, model training, evaluation, and deployment.
Build and operationalize Agentic AI systems (autonomous agents, multi-agent workflows, LLM-based reasoning systems).
Work on time-series forecasting, anomaly detection, and predictive analytics for manufacturing/automotive use cases.
Collaborate with cross-functional teams including data engineering, product, domain experts, and business stakeholders.




Interface with IoT, telematics, MES, ERP, and connected vehicle platforms for data-driven insights.
Ensure scalability and performance by deploying models using cloud-based solutions (Azure/AWS/GCP).
Communicate findings effectively through visualizations, dashboards, and presentations.
Stay current with advancements in AI/ML, including GenAI and Agentic AI ecosystems.

Qualifications
Solid foundation in Statistics & Probability
Hypothesis testing, regression models, A/B testing, Bayesian methods
Expertise in Machine Learning
Supervised & unsupervised learning, model tuning, ensemble techniques
Hands-on experience with AI / Deep Learning
NLP, computer vision, deep neural networks (preferred)
Experience with Agentic AI / Generative AI
LLMs (GPT, Llama, etc.), prompt engineering, RAG, autonomous agents
Proficiency in Python (mandatory)
Libraries: Pandas, NumPy, Scikit-learn, Ten

📌 Dtici Data Scientist Data Operations R&d Bengaluru
🏢 Daimler Truck
📍 Bengaluru