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 | 30 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 How are they all related?
Relation extraction from the Web
Siemens works with thousands of companies. Much like Siemens, many of those companies have complex hierarchies involving subsidiaries and parent companies. The database of Siemens business partners is one of the most valuable assets the company has with a list of almost five million partners all over the world. But there are errors, and missing links! Your challenge: Build a system to decipher existing relationships between any 2 companies by looking up the companies on Wikipedia, their respective websites and on the news. #WebCrawling
Challenge #4 Anomaly Detection
Discover anomalies and reveal strategic trends ahead of the competition. Running and compairing unsupervised to supervised training of a watchdog for financial data, can your trained watchdog detect tipping points?
Your challenge: Detect abnormal bookings, unexpected high (or low) sales of a certain product in a certain region and reveal trends ahead of the competition that may become existential for Siemens Digital Industries. Apply unsupervised and supervised anomaly detection methods for financial time-series. The dataset provided are various time-series of daily bookings (sales, order entries) per country for various product families (exemplary product family: HMI = Human-Machine Interface for factory automation). #AnomalyDetection
Challenge #2 Reconstruct sensor data:
Use AI to track down trains
A smartphone records data on GPS, accelerometer, speed, and time during the ride of a train. When the smartphone works well, the quality of GPS data is good. When the smartphone doesn’t work well, GPS data is missed.
Your challenge: develop a Python function that will restore missed GPS data as accurately as possible, given the data on sensors (timestamp, latitude, longitude, accelerometer_x, accelerometer_y, accelerometer_z, gps_accuracy_in_meters, speed_in_km_per_hour). You can use additional data from google maps or openrailwaymap. #sensordata #reconstructingdata
Challenge #1 Newsroom
Publicly available news articles can contain valuable information that companies like Siemens could use to drive their business forward.
However, the ever-increasing flood of information makes it impossible for individuals to read all the important news articles. In this challenge, you apply NLP algorithms to identify important information about a set of companies in public news articles. #NLP #newscrawling
Challenge #5 Competitive Product Analysis in Healthcare
A competitive analysis is a strategy that entails researching major competitors in order to gain knowledge of their products, sales, and marketing strategies. This information can be used to identify your company's strengths and weaknesses in relation to each competitor. Understanding the competitive advantages of your company's solution against its alternatives is one of the most important factors in being successful in the crowded healthcare market.
Your challenge: Analyse Siemens Healthineers' diagnostic imaging products (CT scanners, MRI devices, ...) as well as those of two of its competitors, GE and Philips. Use web crawling to retrieve available information on the web and apply NLP techniques to understand similarities and differences between the products. #webcrawling #NLP
CFO of Commercial Finance
in Siemens Financial Services
Dr. Michael May
Head of the Siemens Technology Field Business Analytics & Monitoring
Dr. Ariane Sutor
Head of Business Line
in Siemens Digital Industries
Founder of The Globe Team and Coach for First-Time Leaders
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!
All participating teams will receive cash prizes worth a total of €4,000.
Coaches & Speakers
Data Scientist @ SFS Discovery Lab
Senior Key Expert in Algorithms and Applications Development @ Siemens
Data Scientist & NLP Specialist @ Siemens IT Analytics Lab
Data Scientist @ Data Visions, Siemens Digital Industries
Digitalization Expert @ Siemens Healthineers
Founder and CEO of O Canada Tech & President of Women in AI & Robotics
Facilitator, Trainer, Coach, Speaker @ leadventure
Innovation Architect & Coach @ German Entrepreneurship
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,
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 28th of September 2022 at 1 p.m. (CET). After that, you’ll have time till the 30th of September to elaborate on your challenge. The final presentations will start on the 30th of September at 3 p.m. (CET).
What is the setting?
The hackathon will take place virtually. For any questions about this just contact us via firstname.lastname@example.org.
What does the application process look like?
This hackathon is an application-only event. Please apply via our website, 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 21st 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: All participating teams will receive cash prizes worth a total of €4,000.
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: email@example.com