Senior Data Scientist/ML Engineer with recommendation experience for visibility

Maersk, the world’s largest shipping company responsible for moving 20 % of global trade, is on a mission to become the Global Integrator of Container Logistics. To achieve this, we are transforming into an industrial digital giant by combining our assets across air, land, ocean, and ports with our growing portfolio of digital assets to connect and simplify our customer’s supply chain through global end-to-end solutions, all the while rethinking the way we engage with customers and partners.

In this role as senior data scientist/ML engineer on the Global Data and Analytics (GDA) team, you will be working on our new strategic visibility initiative to develop recommendation solutions facing internal and external users. The overall objective is to develop actionable recommendations which enable unprecedented flexibility during supply chain execution and strategic planning and unlock new types of services for our internal users and our customers. Building on top of a best-in-class visibility data foundation, you will be partnering with product managers and engineering counterparts to develop scalable solutions following industry best-practices. You will be empowered to take ownership of your domain and we expect you to proactively contribute to identifying opportunities and solutions. There is a lot of exciting challenges ahead of us, and the ideal candidate will have a passion for working on industry-transforming products and creating impact from the ground up in a fast-paced environment.

You should have demonstrated ability to make sense out of large, integrated datasets, and build statistical and machine learning (ML) models on top of these data sets. Hands-on experience developing recommendation systems and putting solutions to production in collaboration with data engineering and software engineering is crucial for this role. Furthermore, you must have demonstrated experience in initiating and leading ML projects in a dynamic, international environment. Over time, and when you are ready, we will be looking to you to take on more leadership responsibility.

No prior knowledge of logistics needed; we will help you learn what you’ll need to succeed.

Key responsibilities

• Develop, test, and deploy recommendation system solutions and other analytical tools together with the team. In addition to the topics mentioned above, this can range from building integrated data sets over analyses to dashboards and machine learning models
• Lead model development, implementation and deployment end-to-end incl. MLOps (mindset and technical implementation). Specifically, you will drive problem formulation, modeling approach, implementation, testing, deployment and monitoring.
• Collaborate with software engineers and data engineers to deploy recommendation solutions to production
• Partner with product managers to drive maturation of ideas into production solutions, including challenging problem formulation
• Communicate effectively with technical and non-technical audiences

Basic qualifications

• BSc/MSc/PhD in computer science, data science or related discipline with 10+ years of industry experience building cloud-based ML solutions for production at scale, including solution architecture and design experience
• 3+ years of hands-on experience building ML solutions in Python, incl knowledge of common python data science libraries (e.g. scikit-learn, PyTorch, etc)
• Hands-on experience building end-to-end data products based on recommendation technologies
• Experience with collaborative development workflow: version control (we use github), code reviews, DevOps (incl automated testing), CI/CD
• Communication and leadership experience, with experience initiating, driving and delivering projects
• Team player, eager to collaborate

Preferred qualifications

• Experience as tech lead or engineering manager (still hands-on)
• Experience with a common dashboarding technology (we use PowerBI)
• Experience working in cross-functional product engineering teams following agile development methodologies (scrum/Kanban/…)
• Experience with Spark and distributed computing
• Strong hands-on experience with MLOps solutions, including open source solutions.
• Experience with cloud-based orchestration technologies, e.g. Airflow, KubeFlow, etc
• Experience with containerization: Kubernetes & Docker