Analyst Jobs in Cranbrook, BC
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Analyst • cranbrook bc
Last updated: 11 days ago
As a remote, hourly paid R Engineer, you will review AI-generated responses and generate high-quality R and data-analysis-focused content, evaluating the reasoning quality and step-by-step problem-...Show moreLast updated: 11 days ago
If you are looking for an opportunity to contribute your experience, knowledge, and passion in a meaningful way, CLBC is the place for you!.
Community Living British Columbia (CLBC) is a Crown corpo...Show moreLast updated: 30+ days ago
Our typical hiring range will be + / - 10% of the midpoint listed above.Factors influencing this decision include qualifications and market conditions for the role.
BCLC’s two offices are located on t...Show moreLast updated: 30+ days ago
Remote R Engineer - AI Trainer
SuperAnnotateCranbrook, British Columbia, CARemote
Full-time
Quality Service Analyst
Community Living BCCranbrook, BC, CANFull-time
Quick Apply
Senior Programmer Analyst
BCLCRemote within, BC, CARemote
Full-time +1
- Devon, AB (from $ 104,389 to $ 692,483 year)
- Goderich, ON (from $ 140,653 to $ 160,125 year)
- Port Moody, BC (from $ 71,000 to $ 155,000 year)
- Antigonish, NS (from $ 85,235 to $ 153,994 year)
- Renfrew, ON (from $ 78,000 to $ 152,903 year)
- Quesnel, BC (from $ 133,130 to $ 148,124 year)
- Stonewall, MB (from $ 109,158 to $ 145,174 year)
- Weyburn, SK (from $ 47,551 to $ 144,087 year)
Remote R Engineer - AI Trainer
SuperAnnotateCranbrook, British Columbia, CA11 days ago
Job type
- Full-time
- Remote
Job description2+ 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. Previous experience with AI data training or model evaluation is strongly preferred, and Minimum C1 English proficiency is required.
As a remote, hourly paid R Engineer, you will review AI-generated responses and generate high-quality R and data-analysis-focused content, evaluating the reasoning quality and step-by-step problem-solving behind each solution. You will assess solutions for accuracy, clarity, and adherence to the prompt; identify errors in statistical methodology or data-wrangling workflows; fact-check analytical results; write expert-level explanations and model solutions that demonstrate correct use of R; and rate and compare multiple AI responses based on correctness and reasoning quality. This contract role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate that provides AI training data for many of the world’s largest AI companies and foundation model labs, directly helping to improve the world’s premier AI models.
Key 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.
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