Data Scientist
SME Careers • Remote
Education
Bachelor's
Type
Hourly
Pay Rate
$100/hr
Listed
53d ago
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From the SME Careers listing
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.
🌍 Geographic Availability This role is available to qualified experts globally. However, as a U.S.-based company, SME Careers complies with U.S. export control and sanctions regulations. Eligibility depends on your country of residence and any applicable trade restrictions. Some projects may have additional language or region-specific requirements.
💰 Pay Rate May Vary Based on Your Location Your actual compensation will be determined based on your location and local market conditions. See regional job listings for localized pay information, or the application process will clarify your specific rate.
If you're unsure whether you're eligible, proceed with the application—SME's screening will determine your jurisdiction status and applicable compensation.
How long hiring takes
Across the AI training platforms we refer candidates to, the median gap between referral and hire is about 30 days. It varies by platform and role, so treat it as a rough guide for this one.
Interview Prep
Sample questions for a Data Scientist role, written in-house to help you prepare.
How do you determine the required sample size for an experiment before running it?
I run a power analysis based on the minimum effect size that would actually matter for the business decision, the expected baseline variance, and the desired statistical power, typically 80%. Skipping this step is how teams end up running underpowered experiments that can't reliably detect real effects, then wrongly concluding a change had no impact.
What's the difference between correlation and causation, and how do you establish the latter with observational data?
Correlation just means two variables move together, which can be driven by a third confounding factor rather than either variable causing the other. With observational data, I look for natural experiments, instrumental variables, or matching techniques to approximate a controlled comparison, since a true randomized experiment usually isn't available and correlation alone can't support a causal claim.
How would you choose between a simpler statistical model and machine learning approach for a forecasting problem?
For data with clear seasonal and trend patterns and limited history, a statistical model like ARIMA or exponential smoothing is often more reliable than machine learning, which typically needs more data to outperform simpler methods. I'd benchmark both against a holdout period rather than assuming the more complex option is automatically better.
How do you handle a situation where your model's predictions need to be explained to justify individual decisions?
I'd use an inherently interpretable model where possible, or apply a post-hoc explanation method like SHAP values on top of a more complex model if the added performance is worth the extra explanation layer. The explanation needs to be accurate to how the model actually makes decisions, not just a plausible-sounding story layered on afterward.
This listing calls for these tools directly. Prep for the technical screen:
Why this role
This Data Scientist role pays $100 per hour to review AI-generated analytical reasoning, code, and model outputs, checking statistical reasoning and modeling choices with tools named directly in the requirements: Python, NumPy, and scikit-learn. Model evaluation is called out specifically, so the role goes beyond checking whether code runs into judging whether the modeling approach itself is sound. SQL and statistics round out a profile built for someone who has shipped data science work rather than studied it in theory.
Talent pool
We're light on Data Science candidates
We've matched 9 people with a Data Science background against 293 Data Science listings we've tracked, so most go out without one. Set up a profile and we'll consider you for a role like this one.
Set up your profileSkills and categories
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Common questions
How does SME Careers pay freelancers?
Weekly, through Deel. Deel also handles tax compliance, which is how SME Careers pays contractors across more than 40 countries without running its own international payroll.
Is SME Careers work ongoing or project-based?
Project-based. Assignment duration and availability vary by project, but consistently high-quality work earns priority access to whatever comes next.
What does the day-to-day workload look like for elite-expert AI training roles?
Slow and deep, not fast and repetitive. A single task can take 45-60 minutes of researching citations or verifying complex calculations. Quality is what's being measured here, not throughput.
Is AI training work the same as traditional consulting?
No. Instead of client deliverables, you're given complex scenarios to evaluate: grading the AI's logic, correcting its hallucinations, and supplying expert-level reasoning it doesn't have on its own. The job is closer to teaching than consulting.
What does Data Science work look like for a Data Scientist?
Tasks here are scoped to Data Science, not generic labeling. As a Data Scientist, expect to draw on real domain judgment (evaluating outputs, correcting errors, or providing expert reasoning specific to Data Science) rather than following a one-size-fits-all rubric. If you don't have hands-on Data Science background, this is likely not the right listing to start with.
What specific skills does this listing call for?
SQL, Scikit-learn, NumPy, and Model Evaluation are named directly in the listing. If you don't have hands-on experience with these, expect the screening process to test for them directly rather than accepting adjacent experience as a substitute.
How much does this specific role pay?
This listing is posted at $100/hr, a per-task rate. Pay can change between when we last checked the listing and when you apply, so confirm the current number on the platform's own application page before committing time.
What happens when I click Apply on this listing?
You'll be taken to SME Careers's external site to complete your application there. This listing links through a referral, but the process is identical to applying directly; the link just routes you correctly. Create an account on their site and follow their onboarding steps.
Who is behind SME Careers?
SME Careers is the expert talent division of SuperAnnotate, an AI data infrastructure platform, connecting domain experts with high-level AI training projects.