We are looking for an experienced Applied Data Scientist with expertise in building agentic systems and autonomous agents to join one of our R&D. You will be at the core of transforming our supply chain solutions into a fully agentic platform – designing and building agents that autonomously generate analytical pipelines, orchestrate multi-step reasoning, and resolve complex logistics challenges for our customers.
You will combine strong machine learning and deep learning expertise with the ability to architect and implement production-grade agentic systems, working closely with engineering, product, and domain experts to push the boundaries of what autonomous AI can do in supply chain.
Responsibilities:
Design and build autonomous agentic systems that generate, configure, and execute analytical pipelines to solve supply chain challenges end-to-end
Architect multi-agent workflows with planning, tool use, memory, and feedback loops – enabling agents to reason, adapt, and improve over time
Develop and integrate ML and deep learning models (e.g., predictive models, anomaly detection, demand forecasting) as core capabilities within agentic pipelines
Research and apply state-of-the-art techniques in agentic AI, LLM orchestration, and multi-agent systems to production use cases
Translate complex logistics and supply chain challenges into agent-based problem formulations, collaborating closely with product and domain experts
Define and implement rigorous evaluation frameworks for agent performance: correctness, reliability, robustness, and edge-case handling
Collaborate with software engineers to productionize agentic solutions – including testing, monitoring, versioning, and iterative improvement
Contribute to team practices: reproducible code, experiment tracking, documentation, and knowledge sharing
You will combine strong machine learning and deep learning expertise with the ability to architect and implement production-grade agentic systems, working closely with engineering, product, and domain experts to push the boundaries of what autonomous AI can do in supply chain.
Responsibilities:
Design and build autonomous agentic systems that generate, configure, and execute analytical pipelines to solve supply chain challenges end-to-end
Architect multi-agent workflows with planning, tool use, memory, and feedback loops – enabling agents to reason, adapt, and improve over time
Develop and integrate ML and deep learning models (e.g., predictive models, anomaly detection, demand forecasting) as core capabilities within agentic pipelines
Research and apply state-of-the-art techniques in agentic AI, LLM orchestration, and multi-agent systems to production use cases
Translate complex logistics and supply chain challenges into agent-based problem formulations, collaborating closely with product and domain experts
Define and implement rigorous evaluation frameworks for agent performance: correctness, reliability, robustness, and edge-case handling
Collaborate with software engineers to productionize agentic solutions – including testing, monitoring, versioning, and iterative improvement
Contribute to team practices: reproducible code, experiment tracking, documentation, and knowledge sharing
Requirements:
4+ years of experience in applied data science or ML in a product environment, with demonstrated experience building agentic systems or autonomous agents
MSc in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related field (or equivalent practical experience)
Proven track record designing and implementing multi-step agentic pipelines, including LLM-based agents, tool use, planning loops, and memory mechanisms
Hands-on experience with agentic frameworks such as LangChain, LangGraph, AutoGen, or equivalent
Strong Python coding skills; familiar with Spark for large-scale, distributed data processing
Experience with LLM APIs (e.g., OpenAI, Anthropic, Bedrock, open-source models) and prompt engineering for agentic use cases
Experience performing rigorous model evaluation (baselines, cross-validation, error analysis) and defining evaluation strategies for agent behavior
Strong communication and collaboration skills; able to work across engineering, product, and supply chain domain experts and iterate fast
Nice to Have (Advantages):
Experience with multi-agent architectures, agent-to-agent communication protocols, and agent orchestration at scale
Experience with cloud platforms (AWS, GCP, Azure) and MLOps practices (CI/CD for ML, model monitoring, drift detection)
Familiarity with containerization and production engineering practices (Docker, Kubernetes)
4+ years of experience in applied data science or ML in a product environment, with demonstrated experience building agentic systems or autonomous agents
MSc in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related field (or equivalent practical experience)
Proven track record designing and implementing multi-step agentic pipelines, including LLM-based agents, tool use, planning loops, and memory mechanisms
Hands-on experience with agentic frameworks such as LangChain, LangGraph, AutoGen, or equivalent
Strong Python coding skills; familiar with Spark for large-scale, distributed data processing
Experience with LLM APIs (e.g., OpenAI, Anthropic, Bedrock, open-source models) and prompt engineering for agentic use cases
Experience performing rigorous model evaluation (baselines, cross-validation, error analysis) and defining evaluation strategies for agent behavior
Strong communication and collaboration skills; able to work across engineering, product, and supply chain domain experts and iterate fast
Nice to Have (Advantages):
Experience with multi-agent architectures, agent-to-agent communication protocols, and agent orchestration at scale
Experience with cloud platforms (AWS, GCP, Azure) and MLOps practices (CI/CD for ML, model monitoring, drift detection)
Familiarity with containerization and production engineering practices (Docker, Kubernetes)
This position is open to all candidates.
