Do you love big data and analytics? Do you have a strong passion for empirical research and for answering hard questions with data? If yes, you will love working at MiNODES. At the core of our data analytics pipeline we apply advanced machine learning techniques to position people inside our customers’ stores based on wifi signals. We are constantly improving these algorithms and are experimenting with new state-of-the art technologies (e.g. deep learning), to deliver the best data quality possible to our customers.
With our Data Science team we work hard to help our clients to become industry leader for the future of retail. Therefore we offer an internship where you can apply current state-of-the-art machine learning models on a challenging dataset in an interesting industry.
Potential Task you will work on during your internship:
- Identify potential weaknesses of our own MiNODES Wifi Positioning System and apply current state-of-the-art (e.g. Deep Learning) methods to overcome them.
- Build new use cases around clients problems that we’re able to solve by the use of big data and machine learning techniques
- Improve our data quality monitoring infrastructure and help to improve our internal QA Processes
During your time at MiNODES you will be supported by our development and data science team.
Skills & Requirements
We’re looking for preferably graduate students from statistics, computer science, business informatics, or related fields with:
- Excellent statistical intuition and knowledge of various analytical approaches
- Excellent communication skills
- At least theoretical knowledge of machine learning techniques (both supervised and unsupervised learning)
- Ability to transfer theoretical concepts to practical implementations
- Proficient in at least one programming language
- Ability to write efficient SQL statements
Not a must, but we appreciate the following skills:
- Proficient in Python
- Experience with Scikit-learn, pandas, scipy, numpy and tensor flow
- Knowledge of advanced machine learning techniques such as: deep learning, unsupervised outlier analysis, support vector machines, random forests, etc