Data Scientist (Masters)

About The Role

What if your deep knowledge of machine learning, statistics, and data engineering could directly shape how the world's most advanced AI systems reason and solve problems? We're looking for Masters-level data scientists to challenge, audit, and refine cutting-edge AI models — exposing their blind spots and helping build smarter, more reliable systems.

This is a fully remote, flexible contract role. No prior AI industry experience needed — just rigorous domain expertise and a sharp eye for technical quality.

• Organization: Alignerr
• Type: Hourly Contract
• Location: Remote
• Commitment: 10–40 hours/week

• Design Advanced Challenges: Create complex, domain-rich data science problems spanning hyperparameter optimization, Bayesian inference, cross-validation strategies, dimensionality reduction, and more — problems that genuinely stress-test AI reasoning
• Author Ground-Truth Solutions: Develop rigorous, step-by-step reference solutions including Python/R scripts, SQL queries, and mathematical derivations that serve as the definitive benchmark for AI outputs
• Audit AI-Generated Code: Evaluate code produced by AI models using libraries like Scikit-Learn, PyTorch, and TensorFlow — assessing correctness, efficiency, and best practices
• Identify Reasoning Failures: Spot logical flaws in AI outputs such as data leakage, overfitting, improper handling of imbalanced datasets, and flawed statistical conclusions
• Provide Structured Feedback: Document failure modes clearly and systematically so model teams can directly improve AI reasoning and reliability

• Pursuing or holding a Masters or PhD in Data Science, Statistics, Computer Science, or a quantitative field with heavy emphasis on data analysis
• Strong foundational expertise in supervised/unsupervised learning, deep learning, statistical inference, or big data technologies (Spark, Hadoop, etc.)
• Able to communicate complex algorithmic concepts and statistical results clearly in writing
• Naturally detail-oriented — you catch errors in code syntax, mathematical notation, and statistical reasoning that others miss
• Self-motivated and comfortable working independently on technical tasks
• No prior AI or data annotation experience required

• Prior experience with data annotation, data quality assurance, or model evaluation systems
• Proficiency in production-level data science workflows such as MLOps or CI/CD for models
• Familiarity with NLP techniques or large language model evaluation
• Background in academic research or technical writing

• Work directly with industry-leading AI research labs on genuinely frontier problems
• Fully remote and flexible — work when and where it suits you
• Freelance autonomy with meaningful, intellectually stimulating task-based work
• Make a tangible impact on how AI understands and solves complex data science problems
• Potential for ongoing work and contract extension as new projects launch