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Quantitative developer Jobs in Spruce Grove, AB
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Quantitative developer • spruce grove ab
Remote Senior Data Scientist - AI Trainer
SuperAnnotateSpruce Grove, Alberta, CAFresh / Frozen and Care & Treats Lead
MarsAcheson, Alberta, CanadaSenior Analyst
Superior PropaneAlberta, Canada- Montreal-Ouest, QC (from $ 148,407 to $ 276,750 year)
- Montreal, QC (from $ 148,407 to $ 276,750 year)
- Quebec City, QC (from $ 138,125 to $ 247,500 year)
- Vancouver, BC (from $ 100,000 to $ 156,952 year)
- North York, ON (from $ 93,850 to $ 156,000 year)
- Mississauga, ON (from $ 115,205 to $ 155,786 year)
- Old toronto, ON (from $ 108,939 to $ 155,140 year)
- Toronto, ON (from $ 108,396 to $ 155,048 year)
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Remote Senior Data Scientist - AI Trainer
SuperAnnotateSpruce Grove, Alberta, CA- Full-time
- Remote
As a Senior Data Scientist, you will work remotely on an hourly paid basis to review AI-generated analytical reasoning, code, and model outputs, as well as generate high-quality reference solutions and explanations for complex data problems. You will assess solutions for accuracy, clarity, and adherence to the prompt; identify errors in methodology, modeling choices, or statistical reasoning; fact-check quantitative claims; write clear, step-by-step explanations and model solutions that demonstrate correct methods; and rate and compare multiple AI responses based on correctness and reasoning quality.
This is a fully remote, hourly paid contractor role with SME Careers, a fast-growing AI data services company and subsidiary of SuperAnnotate that provides AI training data to many of the world’s largest AI companies and foundation model labs. Your data science expertise will directly contribute to improving the world’s premier AI models used across products and industries.
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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