Transporeon is a SaaS company founded in 2000 in Ulm, Germany. The company provides logistics solutions across several areas, including:
- Buying & selling of logistics services
- Organizing shipment execution
- Organizing dock, yard, truck, and driver schedules
- Invoice auditing for logistics services
It has grown significantly over the years, reaching €150m in revenue before being acquired by Trimble for $2 billion USD in 2022. Transporeon has one of the largest networks of shippers and carriers in Europe, with approximately 1,400 employees: https://www.transporeon.com/en
As our Senior Data Scientist/Machine Learning Engineer you will design, build, and deploy complete data science solutions that span the entire development lifecycle from initial business problem definition to production model monitoring and optimization for diverse business domains including price prediction, intelligent automation workflows, LLM-powered applications, and other AI-driven solutions across our various product groups (e.g., Autonomous Procurement, Autonomous Quotation, Freight Marketplace, Market Insights).
Main tasks and responsibilities:
- Data Analysis & Feature Engineering: Conduct comprehensive data analysis, statistical investigation, and feature engineering to understand data patterns and prepare high-quality datasets for machine learning applications. Design and implement data transformation pipelines and quality frameworks.
- End-to-End Model & Pipeline Development: Execute the complete lifecycle of machine learning models and their associated automated ML pipelines, including design, development, validation, deployment, monitoring, and maintenance across diverse business domains including price prediction, LLM applications, and automation workflows.
- Technical Problem Solving & Innovation: Solve complex analytical and technical challenges related to model performance, data drift, scalability, and implementation. Research, prototype, and evaluate new machine learning techniques, algorithms, and platforms relevant to business challenges.
- MLOps Implementation & Pipeline Management: Apply MLOps best practices to develop, optimize, and monitor ML pipelines, ensuring robust and reliable model deployment and iteration processes.
- Cross-Functional Collaboration & Communication: Collaborate effectively with product managers, software engineers, data engineers, and business stakeholders. Communicate technical concepts, model behaviors, and data insights to support decision-making processes through clear documentation and presentations.
- Knowledge Sharing & Team Development: Share technical expertise and best practices with team members, contribute to onboarding new colleagues, and participate in cross-training initiatives. Support the implementation and adherence to data science best practices within the team.
- Cloud Platform Utilization: Effectively utilize cloud platforms (e.g., AWS, Azure, GCP) and associated services for developing, deploying, scaling, and managing machine learning models and infrastructure.
- Data Quality & Governance Implementation: Ensure the quality, integrity, and suitability of data used for modeling. Implement models and pipelines in accordance with established data governance policies, model governance frameworks, and responsible AI principles.
We expect you to bring...
- Relevant experience as Data Scientist or Machine Learning Engineer. Proven track record of successfully delivering data science projects from problem definition to deployment and monitoring.
- Strong engineering skills in Python and related common libraries and frameworks. Proficient in querying and manipulating large datasets.
- Experience with cloud platforms (Azure is an advantage as we are planning migration from AWS to Azure).
- Ability to clearly articulate complex technical concepts and findings in English to both technical and non-technical stakeholders.
- Organizational skills and self-discipline to work effectively in cross-functional distributed teams.
- Eagerness to stay updated with the latest advancements in in your focus field and AI development.
- Willingness to align with our mission and company values.
Our Inclusiveness Commitment
We believe in celebrating our differences. That is why our diversity is our strength. To us, that means actively participating in opportunities to be inclusive. Diversity, Equity, and Inclusion have guided our current success while also moving our desire to improve. We actively seek to add members to our community who represent our customers and the places we live and work.
We have programs in place to make sure our people are seen, heard, and welcomed and most importantly that they know they belong, no matter who they are or where they are coming from.
