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.
- 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.
- Strong 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, TensorFlow/PyTorch
- Experience with Data Platforms
- Snowflake / Databricks / Spark / SQL
- Experience in Model Deployment
- APIs, Docker, MLflow, CI/CD pipelines
- Familiarity with Cloud Platforms
- Azure (preferred), AWS, or GCP
- Experience in Automotive or Manufacturing domain, including:
- Predictive maintenance
- Quality analytics & defect detection
- Supply chain optimization
- Production planning & optimization
- Connected vehicle / telematics analytics
- IoT data processing
- Strong analytical and problem-solving mindset
- Ability to explain complex models to non-technical stakeholders
- Excellent communication and storytelling skills
- Team-driven mindset with stakeholder management experience
Preferred Qualifications :-
- Experience working with streaming data (Kafka, Spark Streaming)
- Knowledge of Digital Twins / Industry 4.0 concepts
- Exposure to MLOps frameworks
- Experience with graph-based AI or multi-agent systems
- Understanding of data governance and model explainability
At Daimler Truck, we promote diversity and foster an inclusive corporate culture. We value the individual strengths of our employees, as these lead to the best team performance and thus to the success of our company.
Inclusion and Equal opportunities are important to us – regardless of where you come from and who you are. We look forward to receiving applications from people of all cultures and genders, parents, people with disabilities and people from the LGBTIQ+ community.
