Data Science Expert - AI Content Specialist
Posted 1 week ago by Alignerr
Negotiable
Undetermined
Remote
Cambridge, England, United Kingdom
Summary: The Data Science Expert — AI Content Specialist role involves collaborating with AI research labs to design complex data science challenges and audit AI-generated solutions. This fully remote position is tailored for data scientists and quantitative specialists who wish to contribute meaningfully while maintaining a flexible schedule. Candidates will engage in tasks such as developing reference solutions, evaluating AI code, and identifying reasoning failures in AI models. The role offers the opportunity to work with cutting-edge AI technologies and contribute to significant advancements in the field.
Key Responsibilities:
- Design Advanced Challenges — Create complex, domain-rich data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more
- Author Ground-Truth Solutions — Develop rigorous, step-by-step reference solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as the benchmark for AI outputs
- Audit AI-Generated Code — Evaluate model-generated code using libraries like Scikit-Learn, PyTorch, and TensorFlow for correctness, efficiency, and best practices
- Identify Reasoning Failures — Spot logical flaws in AI reasoning — data leakage, overfitting, improper handling of imbalanced datasets — and provide structured feedback to improve model reasoning
- Stress-Test Model Limits — Probe AI responses on topics like neural network architectures, statistical inference, and data engineering pipelines to surface and document failure modes
Key Skills:
- Holds or is pursuing a Master's or PhD in Data Science, Statistics, Computer Science, or a related quantitative field
- Strong foundational knowledge in supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP
- Able to communicate complex algorithmic concepts and statistical results clearly in written form
- Detail-oriented — precise when reviewing code syntax, mathematical notation, and the validity of statistical conclusions
- Self-directed and comfortable working independently in an async environment
- No prior AI or annotation experience required
- Experience with data annotation, data quality review, or evaluation systems (nice to have)
- Familiarity with production-level data science workflows — MLOps, CI/CD for models, or model monitoring (nice to have)
- Background in academic research or technical writing (nice to have)
Salary (Rate): £32.00 hourly
City: Cambridge
Country: United Kingdom
Working Arrangements: remote
IR35 Status: undetermined
Seniority Level: undetermined
Industry: IT
Data Science Expert — AI Content Specialist
About The Role
What if your deep knowledge of machine learning, statistical modeling, and data engineering could directly shape how the world's most advanced AI systems think and reason? We're looking for Data Science Experts to work alongside leading AI research labs, designing complex technical challenges and auditing AI-generated solutions to make frontier models smarter, more rigorous, and more reliable. This is a fully remote, flexible contract role built for practicing data scientists, researchers, and quantitative specialists who want to do meaningful work on their own schedule.
Organization: Alignerr
Type: Hourly Contract
Location: Remote
Commitment: 10–40 hours/week
What You'll Do
- Design Advanced Challenges — Create complex, domain-rich data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more
- Author Ground-Truth Solutions — Develop rigorous, step-by-step reference solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as the benchmark for AI outputs
- Audit AI-Generated Code — Evaluate model-generated code using libraries like Scikit-Learn, PyTorch, and TensorFlow for correctness, efficiency, and best practices
- Identify Reasoning Failures — Spot logical flaws in AI reasoning — data leakage, overfitting, improper handling of imbalanced datasets — and provide structured feedback to improve model reasoning
- Stress-Test Model Limits — Probe AI responses on topics like neural network architectures, statistical inference, and data engineering pipelines to surface and document failure modes
Who You Are
- Holds or is pursuing a Master's or PhD in Data Science, Statistics, Computer Science, or a related quantitative field
- Strong foundational knowledge in supervised/unsupervised learning, deep learning, big data technologies (Spark/Hadoop), or NLP
- Able to communicate complex algorithmic concepts and statistical results clearly in written form
- Detail-oriented — precise when reviewing code syntax, mathematical notation, and the validity of statistical conclusions
- Self-directed and comfortable working independently in an async environment
- No prior AI or annotation experience required
Nice to Have
- Experience with data annotation, data quality review, or evaluation systems
- Familiarity with production-level data science workflows — MLOps, CI/CD for models, or model monitoring
- Background in academic research or technical writing
Why Join Us
- Work directly with cutting-edge large language models and frontier AI research teams
- Fully remote and asynchronous — work when it suits you, from anywhere
- Freelance autonomy with meaningful, intellectually stimulating task-based work
- Contribute to AI development that shapes how models reason about real data science problems
- Potential for ongoing work and contract extension as new projects launch