Senior AI Leadership Role

Talent Seed • Germany
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AI Summary

This senior AI leadership role involves improving core machine learning systems, building intelligent decision-making AI, and driving measurable operational efficiency across the business. The ideal candidate will have 7-12+ years of experience in data science and machine learning, with a strong background in statistical and ML approaches. They will also have experience leading teams and deploying ML systems into production environments.

Key Highlights
Improve core machine learning systems
Build intelligent decision-making AI
Drive measurable operational efficiency
Key Responsibilities
Owning and evolving the forecasting and optimisation engine
Improving machine learning model accuracy across demand and inventory use cases
Designing and deploying agentic AI systems
Technical Skills Required
Python Machine learning frameworks Data pipelines Regression Tree-based models Agentic AI systems Decision automation Optimisation Reinforcement learning Advanced ML approaches
Benefits & Perks
High visibility, high ownership, and direct access to leadership
Opportunity to solve complex, non-trivial ML challenges
Work with large-scale, high-quality datasets
Nice to Have
Experience with agentic AI systems or decision automation
Exposure to optimisation, reinforcement learning, or advanced ML approaches
Background in operations-heavy environments

Job Description


**This role requires relocation to Riyadh, KSA**


About the Client

Our client is building an AI-first operating system for a critical, real-world industry, connecting multiple stakeholders into a single intelligent platform.


The business is already operating at scale, with thousands of active users and live enterprise deployments across multiple markets, integrating transactional, operational, and inventory data into one ecosystem.


They are now entering the next phase of growth:

👉 Evolving from predictive analytics into agentic AI systems that can autonomously make and execute decisions.


The Opportunity

This is a senior, hands-on AI leadership role with a clear mandate:

  • Improve core machine learning systems
  • Build intelligent, decision-making AI
  • Drive measurable operational efficiency across the business


What You’ll Be Responsible For

  • Owning and evolving the forecasting and optimisation engine
  • Improving machine learning model accuracy across demand and inventory use cases
  • Designing and deploying agentic AI systems that:
  • Reduce manual intervention and inefficiencies
  • Solving complex challenges such as low-frequency / sparse data forecasting
  • Building and scaling production-grade ML systems and pipelines
  • Working with large, multi-source datasets across operational systems
  • Driving AI adoption across multiple business functions
  • Leading a lean, high-impact data science team, while staying hands-on


Experience required:

  • 7–12+ years in Data Science / Machine Learning (strong hands-on expertise)
  • Proven experience building and improving forecasting models
  • Strong experience with statistical and ML approaches (e.g., regression, tree-based models)
  • Ability to choose and apply the right model to complex, real-world problems
  • Experience deploying ML systems into production environments
  • Strong coding skills (Python, ML frameworks, data pipelines)
  • Experience leading teams while remaining deeply hands-on


🚫 Not suited for:

  • Purely strategic or consulting profiles
  • Candidates without strong ML fundamentals


Nice to Have

  • Experience with agentic AI systems or decision automation
  • Exposure to optimisation, reinforcement learning, or advanced ML approaches
  • Background in operations-heavy environments (e.g., logistics, marketplaces, etc.)


Why This Role

  • Build AI systems that directly impact real-world operations
  • Work with large-scale, high-quality datasets
  • Own and shape the AI direction of a scaling business
  • High visibility, high ownership, and direct access to leadership
  • Opportunity to solve complex, non-trivial ML challenges


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