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Siemens Female Data Science Network


Solve Exciting AI Challenges at our Virtual Hackathon

Explore the world of Artificial Intelligence within one of the global industry leaders and address exciting AI challenges impacting future needs - 48h | 5 Challenges | 40 High Potentials

Join the Siemens Female Data Science Network in tackling challenges of tomorrow’s industries and business practices. Entering our journey enables you to work with industry experts, provide your expertise and explore new horizons together. This is your opportunity, so come join our network and start hacking.

Experience the best combination of your enthusiasm and expertise in AI together with innovative Siemens departments and specialists. Starting on the 20th of September 2023, we will take you on a two-day journey of exploration and deep dive into different AI activities to give you the insights you need to tackle a specific use case within one of our five challenges.


Exciting opportunities ahead: Join us in solving the industry’s most pressing challenges! You decide where to start!



Challenge #3 Conversational AI for Sustainability Reports

The challenge focuses on building a conversational system utilizing large languages models. The knowledge base of the system are annual sustainability reports and the task is to create a digital assistant that will be able to answer questions all around sustainability in Siemens. More details to follow! #ConversationalAI

Challenge #4 Comparative Analysis of SCADA Systems with ChatGPT

From breweries to car manufacturing and sustainable mobility infrastructure like railroads and electrical power distribution, SCADA systems play a pivotal role in driving automation. SCADA (Supervisory Control and Data Acquisition) systems monitor and control industrial processes or infrastructure on a big scale. 

Our goal is to improve our SCADA solution - with a focus on the human engineer. With the shortage of skilled labor in mind we want to enable anyone working with our SCADA system to configure and unterstand it for increased safety and productivity during operation. With a well-designed SCADA system, we want to create a more socially sustainable workplace.

Your Challenge: To realize all these improvements our developers at Siemens need the right input to understand the SCADA landscape and especially the competitors. Your task is to analyze and compare prominent SCADA systems: Siemens' WinCC, as well as its competitors by e.g. Ignition, Inovance, Beckhoff, Rockwell Automation. Utilize web scraping to gather data and employ the conversational AI, ChatGPT, to interpret and summarize this information.

The ultimate outcome should equip Siemens' product developers with a comprehensive understanding of the SCADA terrain, empowering them to craft more competitive and user-centric products. Together, we strive for a more sustainable future! #WebCrawling #ChatGPT #NLP #SCADA 

Challenge #2 Creating transparency on the Sustainable Business Pipeline


Transparency on portfolio development and its drivers are crucial to the strategy of a financial services provider. This becomes also more and more relevant in terms of sustainability. As Siemens Financial Services (SFS) already has a sustainable reporting in place, we now aim to improve the transparency on our sustainable business pipeline and the underlying conversion rate. Your challenge: Analyse new opportunities within the Sales Tool whether they fulfil SFS’ sustainability criteria ( 1 ) to enable fast identification of sustainable and not beneficial business but also ( 2 ) gather data on common criteria for neutral business to expose potential trends and patterns which could lead to overall improvement of SFS’ sustainability criteria and finally ( 3 ) to create transparency on the sustainable portfolio development to be aligned with agreed business targets. #AI #sustainability

Challenge #1 How to do ML in a sustainable way?


As data scientists, we enjoy doing ML and are convinced of its powers, but know it takes considerable amounts of energy. So here’s your challenge: Figure out the status quo, I.e. what’s the carbon footprint of training and then running different types of ML models? Second, what can we do to reduce this? Compare different model types and programming environments, and third: come up with recommendations. Data for your analysis will be our cloud account consumption. In addition, your research and conceptional work will be needed to take up and solve this challenge. #ML #sustainability

Challenge #5 Identify Labels on Components of Printed Circuit Boards

Making a suppliers product cost structure transparent is an important task when preparing for negotations with the supplier. Identifying the components on a printed circuit board (PCB) takes the major share of work when it comes to understanding the costs of a PCB within the analyzed product. Siemens has developed a tool that helps with the process of identifying and automating this process. Labels printed on top of the components are a vital source to identify the respective component. However, the tool lacks the capability to read these labels yet.

Your task will be to write the code to read the label of a component given an image of this component. #AI #ComputerVision #OCR




Susan Baumann
CFO of Commercial Finance
in Siemens Financial Services

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David Vorih 

CIO of Siemens Financial Services 

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Dietmar Mauersberger
Head of Data Analytics, Business Intelligence and Data Management

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Mona Gindler
Principal at Senovo, 
Ambassador Germany and Mentor at Female Founders

Apply Now
Your Hosts

We Are Looking For:

We are looking for dedicated data enthusiasts, hackers, coders, and entrepreneurial thinkers, who want to develop innovative and creative ideas and find digital/ AI solutions for the industry’s most pressing challenges.


We are looking for female students / PhD students:

  • Who already gained some practical experiences,

  • Who are studying math, physics, data science, business studies or a similar study program

  • Who have experience in analytics and/ or coding (Python/ R)


If you identify yourself as a highly motivated individual with creative thinking and an interest in solving problems, testing concepts, and expanding your horizons, we look forward to hearing from you!

The winning team will receive a cash prize! Stay tuned for more information. 

Your Hosts

Dr. Ulrike Dowie

“Our 5 challenges give proof how applying Data Science takes the everyday work at Siemens to another level. Want to join us? Pick the challenge that attracts you most and apply right now!"

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Dr. Sylvia Endres

"I am very proud of this unique hackathon where female talents from all over the world jointly solve the toughest challenges. Use this opportunity to get in touch with like-minded women and experts from different fields and drive tech and diversity forward."

  • LinkedIn Social Icon
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Coaches & Speakers

Coaches & Speakers

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Alina Schlabritz

Challenge #1

Working Student Data Science @ Data Visions, Siemens Digital Industries

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Henrique Silva

Challenge #2

Machine Learning Engineer


Sabina Przioda


 Data Scientist @ Google


Paul Lukowicz

Challenge #1

Scientific Director @ DFKI

Alexis Guibourgé

Challenge #1

Machine Learning Engineer @ Siemens
Digital Industries

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Christian Barthelme

Challenge #2

Senior Financial Analyst 

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Dr. Markus Geipel

Challenge #4

Technical Lead / Senior Key Expert at Digital Industries

@ Siemens

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Dr. Florian Schmidt

Challenge #5

IT Architect and Tech Lead - Green Digital Ecosystem @Siemens


Katalin Westhoff

Challenge #2

Data Scientist @ Siemens
Discovery Lab

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Elene Mamaladze

Challenge #4

Working Student @ 
Siemens Digital Industries

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Marco Mortier

Challenge #5

Expert Cost and Value Engineering @Siemens


Maria Sukhareva

Challenge #3

Data Scientist & NLP Specialist @ Siemens IT Analytics Lab

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Dr. Ariane Sutor

Challenge #4

Head of Business Line
Siemens Digital Industries

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Stefan Leitol

Challenge #5

Cost and Value Engineer



Siemens Female Data Science Network

"Why is there a “Female Data Science Network” at Siemens? Because we show how connecting data-driven minds in diverse teams leads to great results and makes for a fun working environment!"

Dr. Ulrike Dowie, Dr. Sylvia Endres

Siemens Female Data Science Network



What topics will I work on?

Depending on the track that you are joining, you will dive deeper into the respective topic. Our tracks feature a broad range of different fields like NLP, web crawling, relationship identification, anomaly detection and more. You can choose your track depending on interest and expertise.


Who can apply?

If you are a (preferably master or PhD) student, intern, young professional, university employee or any other AI expert with experience in any of the following fields you are very welcome to join:

  • machine learning / deep learning,

  • natural language processing,

  • explorative & predictive analytics

  • time series analysis & forecasting,

  • and more…

Depending on the track you apply for you should bring at least some of the relevant expertise to the table. We will select the final round of participants depending on skill level. Siemens employees who want to participate in the hackathon outside of work can - of course - join us as well.


When will the hackathon take place?

The kick-off event will take place on the 20th of September 2023 at 1 p.m. (CET). After that, you’ll have time till the 22nd of September to elaborate on your challenge. The final presentations will start on the 22nd of September at 2:30 p.m. (CET).


What is the setting?

The hackathon will take place virtually. For any questions about this just contact us via


What does the application process look like?

This hackathon is an application-only event. Please apply via our website, the application deadline is the 8th of September. We will screen each application thoroughly. Our decision is based on your expertise as well as a fit to the track and the rest of the team. We will let you know by September 11th if you’ll be part of this amazing opportunity. We have limited space, and if we do not choose you – please don’t worry, you can still become part of our AI community later on and join future hackathons.


How can I find a team?

It’s easy to participate and find a team. For each challenge there will be a team with other awesome people based on your interests and skills. If you don’t have a team yet, just apply and choose your preferred challenge. If you already find possible team members before you apply, just let us know who you would like to be in a team within your application.


What data and tools can I work with?

That’s the amazing part – our experts will provide you with everything that is needed for each challenge! Therefore, the data and tools that we provide depend on the specific track you’ll join. If you have suggestions about other useful tools that should be hosted locally, please let us know!


What will be the outcome of my work?

On Friday (September 30th) at 4 p.m. (CET), you will present the final results of your work to our jury in a 10-minute pitch. For this, we highly encourage a code-first mindset – so your presentation should emphasize on the proof of concept, the visualization or the software products of your project and keep the slides at minimum. Of course, we will announce a winner, and there will be prizes.


What do I need to bring?

Just bring your laptop and a happy hacking attitude.


How and by whom are the results evaluated?

A jury of business and technology experts from Siemens will evaluate your solution, based on innovativeness and maturity (working code, technological achievement, user interface, …) and will nominate a winning team.


Can I win anything?

Of course: The winning team will receive a cash prize.

How does Siemens ensure the confidentiality of the internal data on which the challenges are based?

In this hackathon you will have the opportunity to contribute to impactful industrial AI applications, all of which are based on real data from Siemens and our customers. Since this data is confidential and not intended for external publication, we will kindly ask you to sign a non-disclosure agreement as you begin working on your hackathon challenge. For detailed information about licenses and rights, please refer to our Terms & Conditions.

If you have further questions, write us an old-fashioned email and we'll make sure to reach out to you asap:

Female Data Science Network
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