Working Student

vor 5 Monaten

Erlangen, Deutschland Siemens Energy Vollzeit

**Location**: ERL S SP 11

**Mode of Employment**: Part-time / Fixed Term

**A Snapshot of Your Day**

As a Working Student in Data Analytics, you will be part of a dynamic team responsible for analyzing data to provide actionable insights that drive business decisions at Siemens Energy. This role offers a unique opportunity to gain hands-on experience in data analytics, working alongside experienced professionals in the field.

**How You’ll Make an Impact**
- ** Data Collection and Cleaning**: Assist in gathering, cleaning, and preparing data from various sources for analysis
- ** Data Analysis**: Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies in the data
- ** Visualization**: Create visualizations and dashboards using tools like Tableau, Power BI, or similar to present data insights effectively
- ** Reporting**: Assist in preparing reports and presentations that summarize findings and recommendations for stakeholders
- ** Collaboration**: Work closely with data scientists, business analysts, and other stakeholders to understand data requirements and deliver insights
- ** Automation**: Develop scripts and tools to automate data processing and analysis tasks

**What You Bring**
- ** Education**: Currently enrolled in a Bachelor’s or Master’s degree program in Data Science, Statistics, Computer Science, Mathematics, Electrical Engineering, or a related field (ideally master or bachelor studies shortly before graduation)
- ** Technical Skills**:

- Proficiency in programming languages such as SQL, R, or Python
- Familiarity with data visualization tools (e.g., Power BI, Tableau)
- Basic understanding of statistical analysis and machine learning concepts
- Knowledge of data processing and manipulation libraries (e.g., Pandas, NumPy)
- ** Soft Skills**:

- Strong analytical and problem-solving skills
- Attention to detail and ability to work with large datasets
- Good communication and teamwork abilities
- Fluent in English written and spoken
- ** Experience**: Previous internships or project experience in data analytics or related fields, especially in the use of Power BI, is a plus

**About the Team**

Our Grid Technology division enables a reliable, sustainable, and digital grid. The power grid is the backbone of the energy transition. Siemens Energy offers a leading portfolio and solutions in HVDC transmission, grid stabilization and storage, high voltage switchgears and transformers, and digital grid technology.

**Who is Siemens Energy?**

At Siemens Energy, we are more than just an energy technology company. We meet the growing energy demand across 90+ countries while ensuring our climate is protected. With more than 94,000 dedicated employees, we not only generate electricity for over 16% of the global community, but we’re also using our technology to help protect people and the environment.

Our global team is committed to making sustainable, reliable, and affordable energy a reality by pushing the boundaries of what is possible. We uphold a 150-year legacy of innovation that encourages our search for people who will support our focus on decarbonization, new technologies, and energy transformation.

**Our Commitment to Diversity**

Lucky for us, we are not all the same. Through diversity we generate power. We run on inclusion and our combined creative energy is fueled by over 130 nationalities. Siemens Energy celebrates character - no matter what ethnic background, gender, age, religion, identity, or disability. We energize society, all of society, and we do not discriminate based on our differences.

**Rewards/Benefits**
- Exciting insights into an international company
- Lay the foundation for your career with us
- Hands-On Experience: Gain practical experience in a leading energy technology company
- Mentorship: Work under the guidance of experienced professionals and mentors
- Networking: Build a professional network within Siemens Energy and the broader industry
- Flexible Hours: Accommodate your academic schedule with flexible working hours

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