Lead Data Scientist, AI-Driven Supply Chain

Cari

København

Hybrid

DKK 743,000 - 1,092,000

Full time

3 days ago
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Job summary

Novo Nordisk in Denmark is hiring a Lead Data Scientist to set the technical direction for AI, data science, and machine learning across supply chain processes. You will build forecasting, optimization and GenAI use cases end-to-end with a delivery team and take them into production.

The role combines advisory and hands-on development, collaborating with Supply Chain stakeholders and the delivery team to scope problems, define success, and ensure reliable data pipelines and model deployments in

Qualifications

  • Several years of experience developing and deploying data science, machine learning or AI models using real business data.
  • Strong Python and SQL skills, with a solid understanding of model validation, interpretation, uncertainty and business impact.
  • Hands-on experience with data pipelines and medallion architecture, CI/CD, and model deployment.

Responsibilities

  • Set data science direction across the use case portfolio and coach junior data scientists.
  • Develop statistical, ML and AI models for forecasting, network optimisation and workflow automation.
  • Build data and model pipelines in Databricks and take models into production via CI/CD pipelines.
  • Evaluate technical performance and business impact, communicating uncertainty and limitations clearly.
  • Collaborate with AI engineers to integrate models into products and operations.

Skills

Python
SQL
Stakeholder communication
Data modeling

Education

MSc, PhD or equivalent in quantitative field

Tools

Databricks
Unity Catalog
GitHub Actions

Job description

Novo Nordisk in Denmark is hiring a Lead Data Scientist to set the technical direction for AI, data science, and machine learning across supply chain processes. You will build forecasting, optimization and GenAI use cases end-to-end with a delivery team and take them into production.

The role combines advisory and hands-on development, collaborating with Supply Chain stakeholders and the delivery team to scope problems, define success, and ensure reliable data pipelines and model deployments in

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