SeniorFull TimeENMiddle East
Supply chains run the world. We make them run themselves. At Sensos, we turn real-world data into a trusted foundation that enables companies to transform their supply chains into autonomous execution services, acting before problems arise, at scale. We're a fast-moving global team at the intersection of data, agentic AI, autonomous execution, and global supply chains.
As a Data Scientist focused on AI Agents, you will design autonomous agents that monitor real-time supply chain visibility data and execute automated actions directly within Sensos's core platform. You'll own this work end-to-end, from shaping requirements with Product to deploying and evaluating production systems.
Responsibilities:
Design and deploy agent systems. Architect and ship multi-agent workflows and decision-making logic that monitor end-to-end supply chain logistics and trigger automated, real-time responses.
Own evaluation and observability. Build the eval frameworks, monitoring, and safe-rollout practices that let us trust autonomous agents in production - measuring accuracy, drift, and real-world impact against supply chain outcomes.
Translate product needs into systems. Partner directly with Product Management to turn business logic, user needs, and logistics metrics into scalable analytical and agentic solutions.
Build with engineering. Work alongside backend engineers to integrate real-time IoT telemetry and streaming pipelines into agent-driven actions.
Requirements:
BSc or MSc in Computer Science, Data Science, or related field (research experience is an advantage).
3+ years in Data Science, Machine Learning, or a closely related field.
GenAI in production. Proven track record building production GenAI applications: multi-step agents, RAG pipelines, tool-augmented LLMs, and structured outputs.
Strong evaluation and observability practices for LLM systems - this is a must. You've designed eval sets, run regression tests on prompts and agents, monitored production LLM behavior, and made safe-rollout decisions based on real metrics.
Agent orchestration. Hands-on experience designing and orchestrating multi-step or multi-agent systems - managing state, tool use, retries, fallbacks, and handoffs between agents.
Voice agents. Experience building conversational voice agents that handle real-time phone or voice interactions.
Strong ML fundamentals. Solid grounding in classical ML and deep learning - model selection, training, evaluation metrics, handling imbalanced data, feature engineering, and the trade-offs between model families. Comfortable reasoning about when an LLM is the right tool and when it isn't.
Hands-on with the modern agent stack. Proficient in Python. Familiarity with major LLM providers like OpenAI, Azure OpenAI, Google Vertex AI, Anthropic, etc.
Startup mindset. Comfortable working independently in a fast-paced environment, owning ambiguous problems, and finding creative paths forward.
Nice to have
Hands-on experience deploying models on Microsoft Azure (e.g., Azure Foundry, Azure AI Search).
Background in supply chain, logistics, IoT, or other real-time/streaming domains.
