Data Scientist - Supply Chain Optimisation

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 the global leader in high-speed connectivity, Ciena is committed to a people-first approach. Our teams thrive in a culture that prioritizes flexibility, individual growth, well-being, and belonging. The Data Scientist – Supply Chain Optimisation role applies advanced analytics, operations research, machine learning, and statistical modeling to complex global supply chain challenges that directly influence cost efficiency, service performance, and sustainability outcomes.

• Design and implement operations research and optimization models for network design, inventory positioning, production and capacity planning, sourcing, scheduling, and transportation using linear programming, mixed-integer programming, constraint programming, simulation, heuristics, and metaheuristics.
• Develop and evaluate classical machine learning and statistical models, including regression, classification, clustering, time-series forecasting, probabilistic models, and ensemble methods, to improve supply chain performance, cost efficiency, service levels, and sustainability outcomes.
• Analyze, cleanse, and transform large datasets to generate actionable insights.
• Select and apply optimization, machine learning, and statistical approaches to solve complex supply chain problems; validate model performance and assumptions, quantify uncertainty, and communicate trade-offs and recommendations.
• Implement production-ready analytical solutions using Python or comparable programming languages and apply MLOps practices across the model lifecycle, including data and feature pipelines, experiment tracking, versioning, testing, deployment, monitoring, retraining, documentation, and governance.
• Partner with data engineering, architecture, platform, and business teams to develop and maintain machine learning and optimization solutions that are reliable, scalable, explainable, and maintainable in production.
• Create dashboards, presentations, and decision-support outputs that communicate insights and recommendations to technical and non-technical audiences, including senior leaders.

The must haves:

• Education: Degree in Data Science, Statistics, Mathematics, Operations Research, Computer Science, Engineering, or related quantitative discipline.
• Experience: 5+ years of experience in data science, operations research, analytics, or optimization roles in supply chain or similarly complex operational problem domains.
• Application of Python and SQL for data preparation, exploratory analysis, model development, validation, and production-ready analytical solutions.
• Experience developing, validating, and interpreting classical machine learning and statistical models, including regression, classification, clustering, time-series forecasting, probabilistic models, and tree-based or ensemble methods.
• Experience formulating and solving operations research problems using linear programming, mixed-integer programming, constraint programming, simulation, heuristics, or metaheuristics with tools such as OR-Tools, Pyomo, Gurobi, or comparable solvers.
• Ability to determine when optimization, machine learning, statistical modeling, alternative methods, or a combined approach is appropriate and to connect model outputs to practical business decisions.
• Work authorization: Full and unrestricted right to work in the United Kingdom. Ciena is unable to provide employment visa sponsorship for this position now or in the future.

• Background working with modern data platforms such as Snowflake and transformation tools such as dbt.
• Exposure to operationalizing machine learning models through repeatable MLOps practices, including experiment tracking, source and model version control, automated testing, deployment pipelines, model registries, production monitoring, drift detection, retraining, rollback, and lifecycle documentation.
• Experience with cloud-based or enterprise machine learning platforms and related engineering practices, including Dataiku, Databricks, Azure Machine Learning/Azure AI, MLflow, containerization, APIs, CI/CD tooling, and production observability.
• Exposure to deep learning methods and neural network architectures, including feedforward networks, convolutional neural networks, recurrent neural networks, transformers, or graph neural networks, where appropriate for the business problem.
• Exposure to geospatial analytics, graph analytics, or network visualization tools.
• Experience supporting global supply chain environments or complex organizational structures.

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.