Data Scientist - Supply Chain Optimisation

Requirements

• Background working with modern data platforms such as Snowflake and transformation tools such as dbt.
• Exposure to geospatial analytics or network visualization tools.
• Experience supporting global supply chain environments or complex organizational structures.
• Background using Dataiku or Alteryx One.
• #LI-ST1
• At Ciena, we are committed to building and fostering an environment in which our employees feel respected, valued, and heard. Ciena values the diversity of its workforce and respects its employees as individuals. We do not tolerate any form of discrimination.
• Ciena is an Equal Opportunity Employer, including disability and protected veteran status.
• If contacted in relation to a job opportunity, please advise Ciena of any accommodation measures you may require.

Benefits

Additional Information

As the global leader in high-speed connectivity, Ciena is committed to a people-first approach. Our teams enjoy a culture focused on prioritizing a flexible work environment that empowers individual growth, well-being, and belonging. We're a technology company that leads with our humanity-driving our business priorities alongside meaningful social, community, and societal impact. Data Scientist - Supply Chain Optimisation As a Data Scientist within Ciena's Supply Chain Optimisation team, this role applies advanced analytics to complex, global supply chain challenges that directly impact cost, service performance, and sustainability outcomes. The position contributes to data‑driven decision making across procurement, planning, engineering, operations, and logistics functions. This role supports scalable optimization solutions aligned with Ciena's operational priorities. How you will make an impact: Design and implement optimization models addressing network design, inventory positioning, demand forecasting, and transportation efficiency. Apply advanced analytics techniques to improve supply chain performance, cost efficiency, service levels, and sustainability outcomes. Analyze, cleanse, and transform large, imperfect datasets to generate actionable insights. Develop algorithms using optimization, machine learning, and statistical modeling techniques to solve complex supply chain problems. Implement production‑ready analytical solutions using Python, R, or comparable programming languages. Build dashboards and analytical outputs that communicate insights to technical and non‑technical stakeholders. Collaborate with data scientists, analysts, and architects to ensure scalable and aligned analytical solutions. The must haves: Education: Degree in Data Science, Statistics, Mathematics, Operations Research, Computer Science, Engineering, or a related quantitative discipline. Experience: 5+ years of experience in data science, analytics, or optimization roles, including exposure to supply chain problem domains. Application of programming languages such as Python or R for analytical and modeling solutions. Application of machine learning and statistical modeling techniques within supply chain contexts. Utilization of optimization libraries such as OR‑Tools, Pyomo, or Gurobi. Application of SQL or similar tools for data manipulation and analysis. Translation of business problems into analytical approaches that deliver actionable outcomes.

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