Data Scientist (Masters)

Summary

Alignerr is seeking a Masters-level Data Scientist to help audit and refine advanced AI models. The role involves designing complex data science challenges, developing ground-truth solutions, and auditing AI-generated code to improve model reliability.

Responsibilities

• 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

Skills

• 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

Qualifications

Must Haves

• 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

Nice to Haves

• 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

Benefits

• 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