Job
- Level
- Junior
- Job Field
- Data
- Employment Type
- Part Time
- Contract Type
- Temporary employment
- Location
- Bonn
- Working Model
- Hybrid, Onsite
Job Summary
In this role, you will develop statistical methods for analyzing census data, convert SAS scripts into efficient R code, and perform data analyses and automations to enhance analytical potentials.
Job Technologies
Your role in the team
- The Microcensus is the largest annual household survey in Europe: approximately 1% of the population is surveyed regarding their economic and social situation. The focus of Division F36 is the evaluation and analysis of Microcensus data. The main area of responsibility involves the (further) development of estimation and analysis methods for the Microcensus within the framework of data processing and data analysis. The responsibilities also include survey preparation, data processing, and analysis of the supplementary program Housing in the Microcensus, as well as housing variables from the Microcensus subsample on income and living conditions (EU-SILC). Additionally, providing standard and special evaluations, including consulting for various stakeholders from politics, administration, market and social research, and academia, is also part of the division's tasks.
- You assist us with the transition and software migration from SAS to R, including converting SAS scripts into efficient R code.
- You independently conduct data analyses and error diagnostics and implement automations for the data processing of the Microcensus using R and GitLab.
- With you, we will expand our analytical capabilities through the development and application of new methods for processing, analyzing, and publishing microcensus data.
- You contribute your knowledge of statistical methods to the development of imputation and small-area estimation procedures in the Microcensus.
- In the team, you will develop and implement new analysis concepts for the Microcensus and write corresponding reports or articles on the new procedures.
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Our expectations of you
Education
- You are currently pursuing a master's degree or a bachelor's degree and have completed the 2nd semester, or you have either completed commercial vocational training or possess relevant professional experience (e.g., through internships, student jobs) of at least one year.
Qualifications
- You possess excellent knowledge of statistical methods, with a focus on survey statistics, particularly in the areas of sampling design, weighting, and variance estimation.
- You have demonstrable German language skills at least at level C1.
Experience
- You have good knowledge of mathematical and statistical methods as well as practical experience in their application, particularly in distribution models and estimation procedures for statistical modeling (e.g., imputation methods and small-area estimation).
- You have experience and good knowledge in data processing, especially in data preparation, data cleaning, and process automation; very good knowledge of R and Git are essential prerequisites.
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What we offer
- After the onboarding period, you can, for example, perform some of the tasks from home.
- At the same time, you have the opportunity to gain valuable experience in the field of Data Science.
- We consider fair working conditions important: the location, scope, or design of the contractual working hours are coordinated according to the requirements of your studies.
- The classification depends on the individual's qualifications (Bachelor students: max. E7 TVöD; Master students: E8 TVöD).
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Benefits
Work-Life-Integration
Topics You Will Work On
Job Locations
About Your Employer
Statistisches Bundesamt
The Statistical Federal Office is a German federal authority in the portfolio of the Ministry of Interior. It collects, aggregates and examines statistical data on economy, society and environment.
Description
- Company Size
- 250+ Employees
- Company Type
- Established Company
- Working Model
- Hybrid, Onsite
- Industry
- Public Service, Unions
Working Student Data Science
- Location
- Bonn
- Working Model
- Hybrid, Onsite
- Diversity
- Open for all genders
