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AI Superior is a service provider on Data Science, Machine Learning, and AI that helps businesses applying analytics to unlock the potential of data. We are working across multiple verticals executing diverse projects that deal with the visual, audio, sensor, textual and other types of data. We build rapid prototypes, as well as end-to-end products to solve the complex business challenges of our customers.
An internship with AI Superior is a great opportunity to start your career in Data Science. Our interns participate in various data science projects under the mentorship and supervision of more experienced data scientists, learn to work with different machine learning models, frameworks, and tools. They actively contribute to all stages of the project lifecycle: from ideation to component deployment. Those who display good performance and ability to learn would be considered for a full-time position.
Tasks and projects would be assigned to the interns based on their current skills and capabilities they want to develop. Among others, that can be:
Data processing, cleaning, and annotation;
Model development, testing, and fine-tuning;
Machine learning pipeline development, performance validation, and deployment;
Development of packages and modules for internal use and potential productization;
Preparation of presentations, analytical materials, and project summaries.
Full-time remote position
Flexible working hours
Mentoring by experienced colleagues
Recent graduates or current students in a Masters or Ph.D. degree in math, statistics, computer science, or related quantitative field;
Experience using Python and its common data analysis packages (numpy, pandas, matplotlib, scikit-learn);
Good understanding of core machine learning concepts (classification, regression, clustering, score metrics, validation, feature engineering, pipelines);
Experience with data processing: transforming, filtering, and presenting large quantities of data;
Advanced English and German proficiency.
Extra points for:
Experience with deep learning models and frameworks (PyTorch, TensorFlow);
Practical experience with machine learning tasks (NLP, CV, time series, structured data);
Experience with databases (SQL and NoSQL) and clustered data processing (e.g., Hadoop, Spark, Map-reduce), cloud computing services (AWS, GCP, or Azure);
Basic understanding of main software development principles (OOP, VCS, CI/CD);
Domain-specific knowledge relevant for modeling in a particular area (finance, healthcare, media, manufacturing, security, etc.).
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