Overview
We employ over half a million people around the globe across postal services, international express delivery, forwarding and contract logistics. Not surprisingly, our activities generate a lot of data. We believe that our data is the key to unlocking untapped potential in our company – and we are looking for ambitious data scientists and data engineers that can help us enable our digital transformation journey.
Machine learning, mathematical optimization and data engineering are our domains. If you would like to know more about us, check out the information below – and don’t hesitate to give us a call. We are always happy to answer any questions you may have.
Latest Jobs
What we do
Predictive Analytics
We love employing machine learning to give our business a glimpse of the future. What is the warehouse demand next month? At what time will shipments be delivered? Is a particular customer likely to change to a competitor? The questions we get from our business are challenging – and their requirements are ever-changing. The variation in the problems that we solve for our business is also one of the key reasons why our data scientists must be comfortable with time-series forecasting, classification and statistical analysis.
Prescriptive Analytics
Prescriptive analytics is the natural successor to predictive analytics. When we perform predictive analytics, it is (not surprisingly) to tell something about the future. After we have generated insights from our predictive models, we typically venture into prescriptive analytics to help our business partners make the most of this information.
Routing Optimization
As the biggest logistics company in the world, we can also boast one of the most extensive transportation network in the world. However, size comes at the expense of complexity and our data scientists play a significant role in ensuring that shipments are transported both timely and efficiently. Delivering logistics services requires much more information than just point A for pick-up and point B for delivery. What is the most efficient route? Do we optimize on time or cost? What mode of transport should we use? From where should we fly? How do we consolidate? These are just a fraction of the questions we are expected to solve.
How we work
Diversity
Diversity
As a group committed to diversity, data scientists and engineers from all backgrounds and different skill sets bring their expertise to our data science teams. Our data science community consist of physicists, statisticians, natural scientists, computer scientists and engineers from various fields. Each individual brings his or her own expertise to company, to come up with the best solutions.
Data Science Community
Data Science Community
DPDHL Group employs many data scientists among divisions and global locations and we prioritize collaboration across all our divisions and location. We arrange community gatherings such as our regional Lunch and Learn meetings, where our scientists share project experiences, and our annual Data Science Day, where our data scientists get together in Bonn for a full day of internal and external presentations and information exchange. For more academic endeavors, some data scientists have frequent meetings in our Data Science Journal Club, where the topic is cutting-edge data science research.
External Collaborations
External Collaborations
In addition to our internal community, we also have ties with external data science communities. We do
occasional exchanges with the data science departments of other companies on methods and use cases. We
also share our knowledge in public events like regional Meetups.
Do you have an innovative idea for your data science thesis that relates to data analytics and the
logistics industry? Then we look forward to hearing about it. We do occasionally have students working on
their theses on-premise.
Continuous Learning
Continuous Learning
In this challenging world of competition, our data scientists are in full control of their own development – with the sparring and mentorship of our data science colleagues. We bring our new joiners up to speed in our data science bootcamp – covering data science from a business and technical perspective. To keep up with the latest advancements in both literature and technology, our teams participate in workshops and seminars all around the world. Furthermore, we conduct regular internal trainings and participate in external trainings where we see a need for improvement. It is absolutely vital for us that our data scientists and engineers continue to learn – for the sake of themselves and our company.
Corporate Data Lake
Corporate Data Lake
Data is an asset – and, in DPDHL Group, we consider data to be as important an asset as our hubs, planes and trucks. It is imperative for us that all data scientists in the Group have access to data from a reliable and easy to reach source. Our corporate big data infrastructure, more colloquially known as our Data Lake, is currently under implementation – so if you are a data engineer looking for an exceptional opportunity, this is it. We aim to enable our data scientists to deploy their big data and machine learning solutions, with a single click, without worrying about scalability.
Diversity
As a group committed to diversity, data scientists and engineers from all backgrounds and different skill sets bring their expertise to our data science teams. Our international teams consist of physicists, statisticians, natural scientists, computer scientists and engineers from various fields. Each individual brings his or her own expertise to team, to come up with the best solutions.
Data Science Community
DPDHL Group employs many data scientists among divisions and global locations and we prioritize collaboration across all our divisions and location. We arrange community gatherings such as our regional Lunch and Learn meetings, where our scientists share project experiences, and our annual Data Science Day, where our data scientists get together in Bonn for a full day of internal and external presentations and information exchange. For more academic endeavors, some data scientists have frequent meetings in our Data Science Journal Club, where the topic is cutting-edge data science research.
External Collaborations
In addition to our internal community, we also have ties with external data science communities. We do
occasional exchanges with the data science departments of other companies on methods and use cases. We
also share our knowledge in public events like regional meet-ups.
Do you have an innovative idea for your data science thesis that relates to data analytics and the
logistics industry? Then we look forward to hearing about it. We do occasionally have students working on
their theses on-premise.
Continuous Learning
In this challenging world of competition, our data scientists are in full control of their own development – with the sparring and mentorship of our data science colleagues. We give all new joiners continuous onboarding sessions to learn about our past projects – from a business and technical perspective. To keep up with the latest advancements in both literature and technology, our teams participate in workshops and seminars all around the world. Furthermore, we conduct regular internal trainings and participate in external trainings where we see a need for improvement. It is absolutely vital for us that our data scientists and engineers continue to learn – for the sake of themselves and our company.
Corporate Data Lake
Data is an asset – and, in DPDHL Group, we consider data to be as important an asset as our hubs, planes and trucks. It is imperative for us that all data scientists in the Group have access to data from a reliable and easy to reach source. Our corporate big data infrastructure, more colloquially known as our Data Lake, is currently under implementation – so if you are a data engineer looking for an exceptional opportunity, this is it. We aim to enable our data scientists to deploy their big data and machine learning solutions, with a single click, without worrying about scalability.
Exemplary Projects
- Forecasting with
machine learning - Prescriptive
Staff Scheduling - Optimising
deliveries
Forecasting with machine learning
As the national postal service for Germany, last year, we delivered 4.6 million parcels and 59 million letters per working day. Accurately forecasting these volumes is difficult but also crucial for our business. Previously, our letter and parcel business was forecasting their volumes based on human input only.
Because of the expertise of our colleagues in the business, their forecasts were generally quite accurate; however, by employing machine learning, and combining this with the knowledge of our business colleagues, we made significant improvements to their forecasts.
The key players
Anja
Sepideh
Gabriela
As a Data Scientist at DPDHL I enjoy working on a vast range of exciting topics from various
business areas with an immediate and tangible impact, while constantly learning and developing.
On top, doing so within a team of curious and great personalities is especially rewarding.
ANJA
Data Scientist
BSc Cognitive Science, M.Sc. Statistics
Perscriptive staff scheduling
In the earlier predictive analytics example, we first helped the business to forecast volumes using machine learning. With this information, we worked closely with the business to create a prescriptive staff scheduling algorithm to optimize the working schedules for our colleagues in the distribution centers. Thus, the volume forecast helped to meet several objectives for our business.
The key players
Stefan
Felix
The projects and data science problems we face are both challenging and diverse; as a
consequence, I learn something new every day while we generate a measurable impact for the
business. The impact of our projects truly enables us to play an important role in the digital
transformation of our company. All this happens while having fun at work with colleagues from
different cultural and professional backgrounds.
STEFAN
Data Science – Team Lead
Diploma Business Mathematics with major in mathematical optimization
Optimising deliveries
Think back to our previous examples in predictive and prescriptive analytics. We have forecasted the incoming volume of letters and parcels; we have optimized the staff working on-site, now it’s time to deliver. While our previous exercises generated significant value to the business – the delivery of letters and parcels is of utmost importance to any logistics company.
The natural next step was therefore for our operations research colleagues to optimize the routing of our delivery personnel to ensure that the whole process is as efficient as possible. The Traveling salesperson problem is omnipresent in logistics and we are always looking for talented operations researchers to optimize our routing.
The key players
ANDRÈ
Thomas
"We are confronted with a variety of difficult problems for which no standard solution exists.
To effectively tackle these problems we have to combine different methods, which we may already
be familiar with – or that we have to learn by doing research. I really enjoy this challenging
work, which requires a lot of creativity.
ANDRÈ
Data Scientist – Operations Research
PhD Mathematical Optimization
Forecasting with
machine learning
Forecasting with machine learning
As the national postal service for Germany, last year, we delivered 4.6 million parcels and 59 million letters per working day. Accurately forecasting these volumes is difficult but also crucial for our business. Previously, our letter and parcel business was forecasting their volumes based on human input only.
Because of the expertise of our colleagues in the business, their forecasts were generally quite accurate; however, by employing machine learning, and combining this with the knowledge of our business colleagues, we made significant improvements to their forecasts.
Key Players
As a Data Scientist at DPDHL I enjoy working on a vast range of exciting topics from various business areas with an immediate and tangible impact, while constantly learning and developing. On top, doing so within a team of curious and great personalities is especially rewarding.
ANJA
Data Scientist
BSc Cognitive Science, M.Sc. Statistics
I love building machine learning solutions for real-world applications. At DPDHL I get the chance to work on a variety of interesting problems that could be tackled by AI and that have a direct impact on our business processes.
SEPIDEH
Data Scientist
MSc Computer Science – Intelligent Systems
The excitement in Data Science at DPDHL is in the diverse and interesting topics that we drive, the
opportunity to collaborate with amazing colleagues, the openness towards new technologies and
approaches, and the support provided during the whole project journey.
GABRIELA
Data Scientist
MSc Data Mining ]and Knowledge Management
Prescriptive Staff Scheduling
Prescriptive Staff Scheduling
In the earlier predictive analytics example, we first helped the business to forecast volumes using machine learning. With this information, we worked closely with the business to create a prescriptive staff scheduling algorithm to optimize the working schedules for our colleagues in the distribution centers. Thus, the volume forecast helped to meet several objectives for our business.
Key Players
I love building machine learning solutions for real-world applications. At DPDHL I get the chance to work on a variety of interesting problems that could be tackled by AI and that have a direct impact on our business processes.
STEFAN
Data Science – Team Lead
Diploma Business Mathematics with major in mathematical optimization
As a Data Scientist at DPDHL I enjoy working on a vast range of exciting topics from various business areas with an immediate and tangible impact, while constantly learning and developing. On top, doing so within a team of curious and great personalities is especially rewarding.
FELIX
Data Scientist
PhD Physics
Optimising deliveries
Optimising deliveries
Think back to our previous examples in predictive and prescriptive analytics. We have forecasted the incoming volume of letters and parcels; we have optimized the staff working on-site, now it’s time to deliver. While our previous exercises generated significant value to the business – the delivery of letters and parcels is of utmost importance to any logistics company.
The natural next step was therefore for our operations research colleagues to optimize the routing of our delivery personnel to ensure that the whole process is as efficient as possible. The Traveling salesperson problem is omnipresent in logistics and we are always looking for talented operations researchers to optimize our routing.
Key Players
"We are confronted with a variety of difficult problems for which no standard solution exists. To effectively tackle these problems we have to combine different methods, which we may already be familiar with – or that we have to learn by doing research. I really enjoy this challenging work, which requires a lot of creativity.
ANDRÈ
Data Scientist – Operations Research
PhD Mathematical Optimization
Working at DPDHL allows me to solve real problems and to immediately observe the effect of my solutions. I learn something new every day and, what I find especially cool, is the combination of data science and optimization.
THOMAS
Data Scientist – Operations Research
MSc Computer Science, PhD Computer Science
The tools we use
-
At DPDHL, we evaluate each of our projects individually and select the best set of tools that can help us solve the business problems we are facing.
Programming Languages
Machine Learning
Operational Research
Business Intelligence
Big Data
Application process
GET IN TOUCH
Katrin
VP DATA ANALYTICS
Get in touch with me about our projects.
- Email: katrin.koenig@dpdhl.com
- Phone: (+49) 228 1896 3374
- LinkedIn: Katrin König
Gabriela
DATA SCIENTIST
Get in touch with me about our projects.
- Email: katrin.koenig@dpdhl.com
- Phone: (+49) 228 1896 3374
- Kaggle: gabriela_th
Stephan
DATA SCIENTIST
Get in touch with me about our projects.
- Email: katrin.koenig@dpdhl.com
- Phone: (+49) 228 1896 3374
- Github: stephanos73
#dpdhldatascience