Data Scientist – Supply Chain Optimisation

Ciena

Data Scientist utilizing advanced analytics for Supply Chain Optimisation at Ciena. Focused on design, implementation, and improvement of analytics solutions for supply chain challenges.

Posted 6/6/2026full-timeRemote • California, New York • 🇺🇸 United StatesMid-LevelSeniorWebsite

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

optimization modelsdemand forecastingtransportation efficiencyadvanced analytics techniquesdata cleansingdata transformationmachine learningstatistical modelingprogramming languagesSQL

Soft Skills

collaborationcommunicationproblem-solvinganalytical thinking

Tools & Technologies

PythonROR-ToolsPyomoGurobidashboardsdata analysis tools

Industry Keywords

supply chain performancecost efficiencyservice levelssustainability outcomesdata scienceanalyticsoptimizationquantitative discipline

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & 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.

Requirements

What you’ll need

5+ years of experience in data science, analytics, or optimization roles
Degree in Data Science, Statistics, Mathematics, Operations Research, Computer Science, Engineering, or a related quantitative discipline
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.

Benefits

Comp & perks

Flexible work environment
Professional development opportunities
Recognition initiatives