Statistician

Job Type: Contractor
Location: Remote
Openings: 50
Compensation: $60–$120/hr

Required Skills

• Data cleaning and preparation (dirty data)

• Basic descriptive and inferential statistics

• Programming in Python or R for analysis

• Data visualization

• Communicating findings to a non-technical audience

Job Description

We are selecting Statisticians 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.