As a 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.
Important
There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.
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.
Your Profile
Bachelor’s degree or higher in Data Science, Computer Science, Statistics, Mathematics, or a closely related quantitative field.
5+ years of professional experience as a Data Scientist or in a closely related analytical role, working on end-to-end machine learning projects.
Strong proficiency in Python for data analysis and machine learning, including libraries such as pandas, NumPy, scikit-learn, and related tooling.
Solid background in statistics, experimental design, and applied probability, with experience designing and analyzing controlled experiments.
Hands-on experience building, evaluating, and deploying machine learning models in real-world business or product environments.
Advanced SQL skills and comfort working with large, complex datasets from data warehouses or data lakes.
Minimum C1 English proficiency (written and spoken), with the ability to write clear quantitative explanations and follow detailed English-language guidelines.
Experience with data visualization tools or dashboards and the ability to present analytical findings to stakeholders.
Previous experience with AI data training, annotation, or reviewing AI-generated analytical content is a strong plus.
Highly detail-oriented and systematic, with a strong focus on quality, reproducibility, and careful evaluation of reasoning steps.