Our team 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
• Clean, preprocess, and structure complex and messy datasets using advanced statistical software (such as R, Python, SAS, or Stata).
• Apply and document basic descriptive and inferential statistical analyses to uncover trends and patterns in real-world data.
• Develop clear and compelling data visualizations to illustrate key findings and support model development.
• Contribute expertise in dataset annotation, labeling, or enrichment to enhance the quality of AI model training datasets.
• Draft concise, well-organized written summaries of methods, analyses, and results for a non-technical audience.
• Collaborate asynchronously with project stakeholders to clarify requirements, resolve ambiguities, and improve deliverables through effective written and verbal communication.
• Continuously identify data quality issues, provide actionable recommendations, and document solutions for handling dirty or incomplete data.
Preferred Qualifications
• Advanced degree (MS or PhD) in Statistics, Data Science, Mathematics, Biostatistics, or a related quantitative field.
• Expertise in cleaning and preparing complex, messy (“dirty”) datasets with R, Python, SAS, or Stata.
• Proficiency in basic descriptive and inferential statistical techniques, including hypothesis testing and regression analysis.
• Strong programming skills in Python or R for statistical analysis, data manipulation, and visualization.
• Demonstrated ability to communicate complex findings to non-technical and technical audiences with clarity and precision.
• Experience working with large, unstructured, or noisy datasets across a variety of domains.
• Excellent written and verbal communication skills, with a focus on detailed documentation and collaboration in remote environments.
Skills mentioned
Data cleaning and preparation (dirty data)Basic descriptive and inferential statisticsProgramming in Python or R for analysisData visualizationCommunicating findings to a non-technical audience
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