Statistician

RemoteJobsOne; Berlin; Remote; EUR 73.000 - 145.000; Teilzeit

Zusammenfassung

RemoteJobsOne is seeking a Statistician for a fully remote contractor role open to Germany-based candidates. You will contribute expert knowledge to train AI systems, focusing on data-driven solutions and model training with quality real-world input.

Responsibilities include data cleaning, descriptive and inferential analyses, creating visualizations, and documenting methods for a non-technical audience, all while collaborating asynchronously with stakeholders in a remote setting.

Qualifikationen

• Advanced degree in Statistics or related quantitative field.
• Experience cleaning messy datasets with R/Python/SAS/Stata.
• Knowledge of descriptive and inferential statistics incl. hypothesis testing and regression.
• Strong programming skills in Python or R for analysis and visualization.
• Ability to communicate findings to non-technical and technical audiences.
• Experience with large, unstructured/noisy datasets.

Aufgaben

• Clean, preprocess, and structure complex datasets using statistical software.
• Apply descriptive and inferential analyses to real-world data.
• Develop clear data visualizations to illustrate findings.
• Annotate/enrich datasets to improve AI model training data.
• Draft concise written summaries of methods and results for a non-technical audience.
• Collaborate asynchronously with stakeholders to clarify requirements.
• Identify data quality issues and document solutions.

Kenntnisse

Descriptive statistics

Inferential statistics

Data visualization

Communication skills

Data quality & cleaning

Remote collaboration

Big data experience

Ausbildung

MS or PhD in Statistics or related

Tools

R

Python

SAS

Stata

Jobbeschreibung

This is a fully remote position, open to candidates based in Germany.

Pay: $60–$120/hr

Role Title: Statistician

Role Type: Contractor

Location: Remote

the company is selecting Statistician to contribute expert knowledge to a customer project focused on advancing data-driven solutions. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.

Scope of Work

1. Clean, preprocess, and structure complex and messy datasets using advanced statistical software (such as R, Python, SAS, or Stata).
2. Apply and document basic descriptive and inferential statistical analyses to uncover trends and patterns in real-world data.
3. Develop clear and compelling data visualizations to illustrate key findings and support model development.
4. Contribute expertise in dataset annotation, labeling, or enrichment to enhance the quality of AI model training datasets.
5. Draft concise, well-organized written summaries of methods, analyses, and results for a non-technical audience.
6. Collaborate asynchronously with project stakeholders to clarify requirements, resolve ambiguities, and improve deliverables through effective written and verbal communication.
7. Continuously identify data quality issues, provide actionable recommendations, and document solutions for handling dirty or incomplete data.

Preferred Qualifications

1. Advanced degree (MS or PhD) in Statistics, Data Science, Mathematics, Biostatistics, or a related quantitative field.
2. Expertise in cleaning and preparing complex, messy (“dirty”) datasets with R, Python, SAS, or Stata.
3. Proficiency in basic descriptive and inferential statistical techniques, including hypothesis testing and regression analysis.
4. Strong programming skills in Python or R for statistical analysis, data manipulation, and visualization.
5. Demonstrated ability to communicate complex findings to non-technical and technical audiences with clarity and precision.
6. Experience working with large, unstructured, or noisy datasets across a variety of domains.
7. Excellent written and verbal communication skills, with a focus on detailed documentation and collaboration in remote environments.