YO IT Consulting is a fast-growing AI Data Services company that provides AI training data for many of the world’s largest AI companies. They are seeking a remote R Engineer to review AI-generated responses and generate high-quality R and data-analysis-focused content, ensuring accuracy and clarity in statistical methodologies.
Responsibilities:
- Develop AI Training Content: Create detailed prompts in various topics and responses to guide AI learning, ensuring the models reflect a comprehensive understanding of diverse subjects
- Optimize AI Performance: Evaluate and rank AI responses to enhance the model's accuracy, fluency, and contextual relevance
- Ensure Model Integrity: Test AI models for potential inaccuracies or biases, validating their reliability across use cases
Requirements:
- 2+ years of hands-on experience using R for data analysis, statistics, or data science work
- Strong proficiency in R programming, including data wrangling, functional programming patterns, and writing reusable functions or packages
- Solid grounding in applied statistics, including regression, inference, and model validation, with practical experience implementing these methods in R
- Experience building end-to-end analyses in R that include data cleaning, exploratory analysis, modeling, and visualization
- Familiarity with common R ecosystems such as tidyverse, data.table, and ggplot2, and the ability to choose appropriate tools for a given task
- Professional experience in a data-focused role such as data scientist, statistician, quantitative analyst, or similar
- Minimum Bachelor's degree in Statistics, Mathematics, Computer Science, or a closely related quantitative field
- Significant experience using large language models (LLMs) to assist with coding, analysis design, and code review in R
- Excellent English writing skills with the ability to document analyses and explain complex statistical ideas clearly to non-experts
- Minimum C1 English proficiency is required
- Previous experience with AI data training or model evaluation is strongly preferred