Data Scientist with strong background in linear or mixed integer optimization, required for a six-month (hybrid 50%) contract.
As a Data Scientist with deep expertise in Linear Integer Programming and a robust background in analytics and MLOps, you will be instrumental in developing, maintaining, and scaling production-grade optimization models. This role goes beyond just creating plots and presentations; you must be able to deliver clear messages to stakeholders and actively incorporate their feedback back into the AI product.
Because this role involves working within a sophisticated Python codebase, you should have a solid understanding of software architecture and feel comfortable maintaining AI models in a production environment. Google Cloud Platform (GCP) is used for data and deployment workflows, so experience with the GCP ecosystem is essential.
Responsibilities
- Maintain and enhance the existing optimization model by proactively identifying shortcomings and implementing improvements.
- Present model outputs and analytical findings to stakeholders, gather and translate business requirements into technical solutions, and maintain clear, consistent communication with all stakeholders throughout the project lifecycle.
- Ensure the robustness and efficiency of the deployed AI product on GCP through continuous monitoring and maintenance.
- Improve the data preparation and wrangling pipeline, and integrate additional data sources to enrich model inputs.
- Collaborate with Data Scientists, ML Engineers, and Product Managers to improve the performance, reliability, and scalability of the optimization model.
- Implement MLOps best practices for seamless deployment, monitoring, and lifecycle management of machine learning models.
- Conduct ad-hoc data analyses and develop visualizations to support data-driven decision-making across teams.
Required Skills
- Solid knowledge and experience in discrete optimization models (Integer Programming, Mixed Integer Programming)
- Proven experience developing and deploying ML models in the cloud, preferably on GCP (beyond notebook-based coding and execution).
- Solid Python programming skills—writing clean, efficient, modular, and production-ready code that is easy to maintain and test.
- Hands-on experience with MLOps and CI/CD pipelines, ideally on GCP.Strong SQL skills for data manipulation and analysis.
- Ability to understand diverse data sources and build robust data wrangling and aggregation pipelines.Familiarity with DBT for data transformation.
- Comfortable working in Agile, cross-functional teams.
- Strong collaboration skills and ability to thrive in complex, ambiguous problem domains.