Senior Data Scientist

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

Data and Machine Learning (ML) are revolutionizing the way of doing business on a global scale. sennder is a European digital freight forwarder with a data-centric problem-solving approach to building the next generation of supply chain and road logistics services. Do you want to help us to shape the future together?

We are looking for a (Senior) Data Scientist to join our central Machine Learning Engineering team as part of sennder’s Data department. The department’s mission is to “Relentlessly build exceptional value-adding products that inspire data-centricity in everything sennder does”. We’re a large, diverse team of ML/data engineers, data scientists, analysts and technical product people that are passionate by such mission. We want to attract and train world-class talent to form a incredible group that aim to provide you with the most productive and growth-friendly time of your career.

All Data dept. teams are multidisciplinar (e.g.: data engineering, MLOps, Data Science). The teams’ scope ranges from creating a unique technological backbone for our data platform to developing advanced predictive and prescriptive analytical services. Our proprietary data platform enables our operation teams to work in a distributed analytics-as-a-service ecosystem where everyone is empowered to be data driven in every decision they make. Our predictive and prescriptive analytics services bring us an unprecedented competitive advantage in terms of business automation across the industry. The obtained insights are then translated in a better customer experience, enabling a scalability flywheel data and revenues grow exponentially with each other.

Every day, we acquire 3M+ new real-time data points (augmenting by the day!) about the cross-region road logistics industry in Europe. This data is used to build the future of logistics marketplaces where pricing optimization, load-to-carrier recommendation, load search and network optimization happen in an automated fashion.

Can you even imagine where we can go with your help? Let’s #keepOnTrucking... together!

YOUR MISSION:

• Apply data science concepts to solve problems like pricing optimization, load-to-carrier recommendation, load search and logistics network optimization, among others;
• Define the new state-of-the-art for machine learning engineering in the road logistics services;
• Develop machine learning expertise beyond basic supervised and unsupervised learning, mentor and be mentored by other team members;
• Prototype and subsequently operationalize innovative, data-intensive, end-to-end machine-learning-based decision engines, following the latest best practices on MLOps;
• Enforce the best principles in ML System Design by balancing the feedback loop on data exploitation and data acquisition, follow the 80/20 ruling, focus on the right metric in every design decision and once a shippable amount of value is created, go live, evaluate, learn and iterate;
• Demonstrate your leadership skills and manage a Machine Learning based project or product, which you will define through frequent cross-functional interactions with your peers as well with experienced logistics professionals and Technical Product Owners, among other stakeholders;

YOUR PROFILE:

• Highly motivated with excellent communication and strong interpersonal skills;
• M.Sc or PhD in a quantitative field and/or working experience as a Data Scientist or Machine Learning Engineer;
• T-shaped mindset: actual expertise on a single area but the ownership/willingness to contribute to the product end to end in order to ensure a healthy value creation chain;
• Teamaholic. We don’t believe in super-heroes but rather in super-teams: teams that own products and are the single unit of work :)
• Proficiency in Python with ability to translate ideas into prototypes and prototypes into production;
• Experience with working on Machine Learning projects to learn from structured data as well as deep knowledge on statistics;
• Fluent written and verbal communication in English;

BONUS:

• Deep knowledge of the theory and expertise on the application of Machine Learning;
• PhD in Machine Learning and/or data mining fundamental topics and/or non-trivial applications of ML/DM foundations;
• Top tier academic track on ML related conferences (e.g.: KDD, NeurIPS, ICML, AAAI, ECML/PKDD);
• Solid Python and software engineering skills, including best practices like CI/CD and Git;
• Proven experience in contributing to machine learning engineering teams in industry (2+ yrs.);
• Experience on MLOps in modern cloud systems (preferrably, AWS);
• Experience with Agile philosophies (e.g.: Scrum, Scrumban, Kanban, XP) and project management tools (e.g.: JIRA);