14 set - Firenze
Team.Blue
OverviewAs Senior AI Data Scientist you will build agentic systems to run HR processes end to end across multiple countries.
You'll map processes, quantify costs, and design stateful, production-ready agents that can trigger actions and integrate with various systems.
The role emphasizes robust, deterministic automation over pure modelling, with safety gates, rollbacks, and governance.
You will own end-to-end delivery—from concept to containerisation, instrumentation, and maintenance.
This is a high-impact, cross-functional role at scale, shaping how HR operations are automated across Europe.
ResponsabilitàMap leaver offboarding across 19 countries and translate into a process inventory with cost per step
Facilitate workshops with HR, Rewards, and Ops leads to assess automation candidates for impact and feasibility
Design state transitions, triggers, branches, system calls, wait points, and failure handling for multi-step flows
Implement guardrails: dry-run mode, approval gates, least-privilege credentials, rollback paths
Determine human-in-the-loop points, confidence thresholds, and a practical review queue
Develop evals for output beyond accuracy (task completion, hallucinations, bias testing)
Wire agents to webhooks with idempotent handlers to avoid duplicate actions
Decide which steps require frontier models vs. cheaper alternatives and prove routing decisions with data
Evaluate vendor APIs, data models, integration costs, and API exposure in vendor demos
Requisiti fondamentali7+ years building data and ML systems in industry
Expert in Python and ML; production-ready code and containerisation
Experience shipping end-to-end multi-step LLM workflows with orchestration and fault tolerance
Cost and latency engineering mindset: model routing, caching, batching
Strong governance and safety practices: least-privilege, approval gates, rollback
Ability to map real processes with quantification and stakeholder facilitation
Executive-level business case writing and cost modelling
Facilitation with senior stakeholders
Stakeholder management and clear communication
Pragmatic decision-making under high risk
Python with solid readability
Docker or equivalent containerisation
End-to-end ML systems with multi-step tool-calling workflows
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