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Data Science & Engineering

The most advanced predictive analytics solutions effectively unify cutting-edge AI methods and specific process expertise. Your engineering background is a crucial ingredient to contextualize data and improve the effectiveness of our AI solutions. You'll be tasked in developing methods to ingest that knowledge into our machine learning algorithms.


Sarnen / Aachen


Bachelor's/Master's Thesis

What You’ll Do

    Alpamayo, a tech startup and CSEM spinoff, is focused on creating intelligent maintenance and quality solutions for industrial applications. Our goal is to make these solutions more accessible through the development of PREKIT, a framework for process monitoring and predictive analytics. Combining in-depth process expertise with the power of AI, we strive to meet the intricate needs of machine manufacturers.

    PREKIT, our suite of software modules, is designed to encapsulate this approach. It empowers engineering teams to leverage their expert problem understanding to design powerful analytics solutions. The suite assists equipment manufacturers in providing valuable digital services to connected machines globally. Deployable as an end-to-end solution, PREKIT aids users right from initial pilot implementation to running a scalable, fully-managed service. Alternatively, it can seamlessly integrate with customers' existing digital ecosystems.
    As we grow, we're searching for driven engineers to contribute at the core of our technology.

    You will collaborate closely with our CTO in the following tasks and recieve excellent guidance throughout your thesis:

    - Perform a practical study on available types of domain knowledge relevant to data-driven predictive maintenance analytics and develop/refine concepts on embedding this knowledge in data-driven solutions.

    - Implement and evaluate different techniques on real-world datasets.

    - Provide ready-to-integrate modules for our predictive analytics engine based on your results.

Who You are

    - Educational Background: Pursuing a degree in Engineering, Computer Science, or related field.

    - AI & Machine Learning: Basic understanding of AI methodologies.

    - Data Contextualization: Ability to understand, interpret, and contextualize data with respect to engineering and industrial applications.

    - Adaptability: Comfortable in fast-paced environments with the aptitude to learn quickly and pivot tasks when required.

    - Initiative: Willingness to take ownership of tasks, from conception to deployment, and drive projects forward.
    - Proficiency in Python and experience with relevant tools for data analytics, such as Pandas, PyTorch, and SkLearn.
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