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5 дней назад

Data Science Intern / Working Student (AI)

Формат работы
onsite
Тип работы
parttime
Грейд
trainee
Английский
b2
Страна
Germany
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR

Data Science Intern / Working Student (Data Science/ML): Analyzing flight and operational data to improve autonomous systems with an accent on predictive modeling, statistical analysis, and sensor data interpretation. Focus on developing data processing workflows, creating technical visualizations, and validating hypotheses using real-world data.

Location: Munich, Germany

Company

hirify.global is a defense technology company specializing in high-performance, software-defined unmanned autonomous systems to support NATO Allies.

What you will do

  • Perform exploratory data analysis to identify patterns and anomalies in flight and operational data.
  • Develop and evaluate predictive models and statistical algorithms for product development.
  • Create technical visualizations and reports for engineering stakeholders.
  • Collaborate with Flight Science engineers to design experiments and validate hypotheses using sensor data.
  • Build data processing workflows to prepare and enrich datasets for modeling.
  • Document methodologies and support performance metric evaluation for autonomous features.

Requirements

  • Currently pursuing a degree in Data Science, Computer Science, Physics, Mathematics, or related field (minimum 3 semesters completed).
  • Strong Python skills for data analysis and machine learning.
  • Solid understanding of statistics and ML fundamentals.
  • Experience with SQL for relational database management.
  • Proficiency with data visualization libraries (Matplotlib, Plotly) or BI tools.
  • English: Strong written and spoken communication skills required.

Nice to have

  • Experience with deep learning frameworks such as PyTorch or TensorFlow.
  • Familiarity with time-series analysis, sensor data, or signal processing.
  • Basic knowledge of cloud platforms (AWS, Azure, GCP) or Docker.
  • Experience translating analytical findings into practical recommendations for technical stakeholders.

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