About Adidas:
Inspired by our heritage, we push the boundaries of culture and human performance. Through sport, we have the power to change lives. Headquartered in Herzogenaurach, Germany, we employ more than 62,000 people across the globe.
Job Description
- Be part of a team of highly skilled Data Scientists from diverse backgrounds like statistics, mathematics, economics and computer science that develops and factorizes complex data science solutions.
- Apply a range of mathematical, statistical, predictive modelling or machine-learning techniques in consultation with experts, and with sensitivity to the limitations of the techniques.
- Design, code, verify, test, document, amend and refactor programs/scripts.
- Support the investigation of operational needs and problems, and opportunities, contribute to the recommendation of improvements in automated and components of new or changed processes and organization.
- Undertake analytical activities and deliver analysis outputs, in accordance with client needs and conforming to agreed standards.
- Support the selection, acquisition and integration of data for analysis.
- Applies agreed standards and tools, to achieve a well-engineered result. Engage in code reviews.
- Inspire us with the latest knowledge from research and academia.
Eligibility Criteria
- You hold a Master’s degree in Computer Science/Statistics/Econometrics, or related field with a strong quantitative focus (Mathematics/Physics)
- Ideally, you’ve already had first professional experiences (academia/ internships) preferably with a focus on online retailing or sporting goods.
- You have strong professional communication skills (English, written and oral).
- You interact confidently with your teammates and do not shy away from sharing and presenting your work.
Preferred skill:
- In your studies, you’ve have acquired theoretical knowledge of Machine Learning (e.g., tree-based models, NLP, image processing) – now you are eager to put things into practice. Hands-on experience with exploratory data analysis and feature engineering (e.g., in the context of hackathons) would be a strong plus.
- You should have at least basic understanding of statistics/ statistical inference. Knowledge of linear algebra, optimization and time-series modeling would be a plus.
- In your studies/ during internships, you have already worked with Python and its state-of-the-art analytical libraries (e.g., pandas, scikit-learn, PySpark, plotly) – now you are ready to push your programming skills to the next level. You know the basics of Deep Learning architectures (e.g., CNNs or RNNs) and you are happy to experiment with tools like TensorFlow or PyTorch. Basic knowledge of R (including tidyverse) is an advantage.
- Ideally, you’ve already worked in a Jupyterlab/ Notebook environment or with IDEs like PyCharm or Visual Studio Code. Great, if you know how to use Bitbucket/ GIT for version control and collaborative coding.
- You have a basic understanding of relational databases and ideally know how to query large databases (e.g., via Spark, SQL, Hue).
- You are familiar with basic software engineering principles (e.g., integration/ unit testing) and the concept of continuous integration. Experience in the use of Jenkins would be a plus.
- You are strongly interested in our focus areas (i.e., recommender and ranking systems, website personalization, search engine optimization, product ranging, sizing, tactical pricing, demand forecasting, image processing, social media listening, customer lifetime value). Research or project work from those areas would be a strong plus.