Data Science Expert - AI Content Specialist
Posted 1 week ago by Alignerr
Negotiable
Undetermined
Remote
United Kingdom
Summary: The Data Science Expert – AI Content Specialist role involves leveraging expertise in machine learning and data engineering to enhance AI systems. The position focuses on designing complex data science challenges, authoring benchmark solutions, and auditing AI-generated code. This fully remote contract role offers flexibility for data scientists seeking intellectually stimulating work. Candidates are expected to have a strong foundation in data science and the ability to communicate complex concepts effectively.
Key Responsibilities:
- Design Advanced Challenges — Create complex, domain-specific data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more
- Author Ground-Truth Solutions — Develop rigorous, step-by-step technical responses including Python/R scripts, SQL queries, and mathematical derivations that serve as the definitive "golden" benchmark
- Audit AI-Generated Code — Evaluate outputs from models leveraging Scikit-Learn, PyTorch, TensorFlow, and other leading libraries for correctness, efficiency, and best practices
- Refine AI Reasoning — Identify and document failure modes such as data leakage, overfitting, and improper handling of imbalanced datasets, then provide structured feedback to improve model logic
- Document Model Weaknesses — Systematically probe AI reasoning across machine learning theory, statistical inference, neural network architectures, and data engineering pipelines
Key Skills:
- Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a related quantitative field
- Strong foundational knowledge across supervised/unsupervised learning, deep learning, big data technologies (Spark, Hadoop), or NLP
- Able to communicate complex algorithmic concepts and statistical results clearly and precisely in writing
- Highly detail-oriented when reviewing code syntax, mathematical notation, and statistical conclusions
- Self-motivated and comfortable working independently on task-based assignments
- No prior AI or annotation experience required
- Experience with data annotation, data quality workflows, or AI evaluation systems (nice to have)
- Familiarity with production-level data science practices such as MLOps or CI/CD for model deployment (nice to have)
- Prior work auditing or benchmarking machine learning pipelines (nice to have)
Salary (Rate): £32.00 hourly
City: undetermined
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 expertise in machine learning, statistical inference, 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 stress-test cutting-edge language models, author gold-standard solutions, and help push AI reasoning to its limits. This is a fully remote, flexible contract role built for serious data scientists who want meaningful, intellectually stimulating 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-specific data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more
- Author Ground-Truth Solutions — Develop rigorous, step-by-step technical responses including Python/R scripts, SQL queries, and mathematical derivations that serve as the definitive "golden" benchmark
- Audit AI-Generated Code — Evaluate outputs from models leveraging Scikit-Learn, PyTorch, TensorFlow, and other leading libraries for correctness, efficiency, and best practices
- Refine AI Reasoning — Identify and document failure modes such as data leakage, overfitting, and improper handling of imbalanced datasets, then provide structured feedback to improve model logic
- Document Model Weaknesses — Systematically probe AI reasoning across machine learning theory, statistical inference, neural network architectures, and data engineering pipelines
Who You Are
- Pursuing or holding a Master's or PhD in Data Science, Statistics, Computer Science, or a related quantitative field
- Strong foundational knowledge across supervised/unsupervised learning, deep learning, big data technologies (Spark, Hadoop), or NLP
- Able to communicate complex algorithmic concepts and statistical results clearly and precisely in writing
- Highly detail-oriented when reviewing code syntax, mathematical notation, and statistical conclusions
- Self-motivated and comfortable working independently on task-based assignments
- No prior AI or annotation experience required
Nice to Have
- Experience with data annotation, data quality workflows, or AI evaluation systems
- Familiarity with production-level data science practices such as MLOps or CI/CD for model deployment
- Prior work auditing or benchmarking machine learning pipelines
Why Join Us
- Work directly with industry-leading large language models at the frontier of AI research
- Fully remote and asynchronous — work when and where it suits you
- Freelance autonomy with the structure of meaningful, intellectually challenging work
- Contribute to AI development that has a real and lasting impact on how technology reasons through complex problems
- Potential for ongoing work and contract extension as new projects launch